@comment{{This file has been generated by bib2bib 1.97}}
@comment{{Command line: bib2bib ../bibli.bib -c 'subject:"kernel" or keywords:"kernel"' -ob tmp.bib}}
@article{Amari1999Improving,
author = {Amari, S.-I. and Wu, S.},
title = {Improving support vector machine classifiers by modifying kernel
functions},
journal = {Neural {N}etworks},
year = {1999},
volume = {12},
pages = {783--789},
number = {6},
month = {Jul},
abstract = {We propose a method of modifying a kernel function to improve the
performance of a support vector machine classifier. {T}his is based
on the structure of the {R}iemannian geometry induced by the kernel
function. {T}he idea is to enlarge the spatial resolution around
the separating boundary surface, by a conformal mapping, such that
the separability between classes is increased. {E}xamples are given
specifically for modifying {G}aussian {R}adial {B}asis {F}unction
kernels. {S}imulation results for both artificial and real data show
remarkable improvement of generalization errors, supporting our idea.},
pdf = {../local/amar99.pdf},
file = {amar99.pdf:local/amar99.pdf:PDF},
subject = {kernel},
url = {http://www.islab.brain.riken.go.jp/wusi/GKSVM.ps}
}
@inproceedings{Andrews2002Multiple,
author = {Andrews, S. and Hofmann, T. and Tsochantaridis, I.},
title = {Multiple {I}nstance {L}earning with {G}eneralized {S}upport {V}ector
{M}achines},
booktitle = {Proceedings of the {E}ighteenth {N}ational {C}onference on {A}rtificial
{I}ntelligence},
year = {2002},
pages = {943-944},
publisher = {American Association for Artificial Intelligence},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09}
}
@article{Aronszajn1950Theory,
author = {Aronszajn, N.},
title = {Theory of reproducing kernels},
journal = {Trans. {A}m. {M}ath. {S}oc.},
year = {1950},
volume = {68},
pages = {337~-~404},
pdf = {../local/Aronszajn1950Theory.pdf},
file = {Aronszajn1950Theory.pdf:local/Aronszajn1950Theory.pdf:PDF},
keywords = {kernel-theory},
subject = {kernelml}
}
@article{Bach2002Kernel,
author = {Bach, F.R. and Jordan, M.I.},
title = {Kernel independent component analysis},
journal = {J. Mach. Learn. Res.},
year = {2002},
volume = {3},
pages = {1--48},
pdf = {../local/Bach2002Kernel.pdf},
file = {Bach2002Kernel.pdf:local/Bach2002Kernel.pdf:PDF},
html = {http://www.ai.mit.edu/projects/jmlr/papers/volume3/bshouty02a/abstract.html},
subject = {kernel},
url = {http://jmlr.csail.mit.edu/papers/v3/bach02a.html}
}
@article{Bao2002Identifying,
author = {Bao, L. and Sun, Z.},
title = {Identifying genes related to drug anticancer mechanisms using support
vector machine},
journal = {F{EBS} {L}ett.},
year = {2002},
volume = {521},
pages = {109--114},
abstract = {In an effort to identify genes related to the cell line chemosensitivity
and to evaluate the functional relationships between genes and anticancer
drugs acting by the same mechanism, a supervised machine learning
approach called support vector machine was used to label genes into
any of the five predefined anticancer drug mechanistic categories.
{A}mong dozens of unequivocally categorized genes, many were known
to be causally related to the drug mechanisms. {F}or example, a few
genes were found to be involved in the biological process triggered
by the drugs (e.g. {DNA} polymerase epsilon was the direct target
for the drugs from {DNA} antimetabolites category). {DNA} repair-related
genes were found to be enriched for about eight-fold in the resulting
gene set relative to the entire gene set. {S}ome uncharacterized
transcripts might be of interest in future studies. {T}his method
of correlating the drugs and genes provides a strategy for finding
novel biologically significant relationships for molecular pharmacology.},
pdf = {../local/bao02.pdf},
file = {bao02.pdf:local/bao02.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://www.elsevier.com/febs/402/19/42/article.html}
}
@article{Ben-Hur2001Support,
author = {Ben-Hur, A. and Horn, D. and Siegelmann, H.T. and Vapnik, V.},
title = {Support {V}ector {C}lustering},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2001},
volume = {2},
pages = {125--137},
pdf = {../local/benh01.pdf},
file = {benh01.pdf:local/benh01.pdf:PDF},
subject = {kernel},
url = {http://www.ai.mit.edu/projects/jmlr/papers/volume2/horn01a/rev1/horn01a1r.pdf}
}
@article{Bock2001Predicting,
author = {Bock, J. R. and Gough, D. A.},
title = {Predicting protein-protein interactions from primary structure},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {455--460},
number = {5},
pdf = {../local/bock01.pdf},
file = {bock01.pdf:local/bock01.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/5/455.pdf}
}
@inproceedings{Borgwardt2005Shortest-Path,
author = {Karsten M. Borgwardt and Hans-Peter Kriegel},
title = {Shortest-Path Kernels on Graphs},
booktitle = {{ICDM} '05: {P}roceedings of the {F}ifth {IEEE} {I}nternational {C}onference
on {D}ata {M}ining},
year = {2005},
pages = {74--81},
address = {Washington, DC, USA},
publisher = {IEEE Computer Society},
doi = {http://dx.doi.org/10.1109/ICDM.2005.132},
pdf = {../local/Borgwardt2005Shortest-Path.pdf},
file = {Borgwardt2005Shortest-Path.pdf:Borgwardt2005Shortest-Path.pdf:PDF},
isbn = {0-7695-2278-5},
keywords = {chemoinformatics kernel-theory}
}
@inproceedings{Boser1992training,
author = {Boser, B. E. and Guyon, I. M. and Vapnik, V. N.},
title = {A training algorithm for optimal margin classifiers},
booktitle = {Proceedings of the 5th annual {ACM} workshop on {C}omputational {L}earning
{T}heory},
year = {1992},
pages = {144--152},
address = {New York, NY, USA},
publisher = {ACM Press},
pdf = {../local/bose92.pdf},
file = {bose92.pdf:local/bose92.pdf:PDF},
location = {Pittsburgh, Pennsylvania, United States},
subject = {kernel},
url = {http://www.clopinet.com/isabelle/Papers/colt92.ps.Z}
}
@article{Burges1998Tutorial,
author = {Burges, C. J. C.},
title = {A {T}utorial on {S}upport {V}ector {M}achines for {P}attern {R}ecognition},
journal = {Data {M}in. {K}nowl. {D}iscov.},
year = {1998},
volume = {2},
pages = {121-167},
number = {2},
pdf = {../local/burg98.pdf},
file = {burg98.pdf:local/burg98.pdf:PDF},
subject = {kernel},
url = {http://www.kernel-machines.org/papers/Burges98.ps.gz}
}
@book{Cristianini2000introduction,
title = {An introduction to {S}upport {V}ector {M}achines and other kernel-based
learning methods},
publisher = {Cambridge University Press},
year = {2000},
author = {Cristianini, N. and Shawe-Taylor, J.},
subject = {kernel},
url = {http://www.support-vector.net}
}
@article{Cuturi2005Semigroupa,
author = {Cuturi, M. and Fukumizu, K. and Vert, J.-P.},
title = {Semigroup kernels on measures},
journal = {J. Mach. Learn. Res.},
year = {2005},
volume = {6},
pages = {1169-1198},
pdf = {../local/Cuturi2005Semigroupa.pdf},
file = {Cuturi2005Semigroupa.pdf:Cuturi2005Semigroupa.pdf:PDF},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v6/cuturi05a.html}
}
@article{Ding2001Multi-class,
author = {Ding, C.H.Q. and Dubchak, I.},
title = {Multi-class protein fold recognition using support vector machines
and neural networks},
journal = {Bioinformatics},
year = {2001},
volume = {17},
pages = {349--358},
abstract = {Motivation: {P}rotein fold recognition is an important approach to
structure discovery without relying on sequence similarity. {W}e
study this approach with new multi-class classification methods and
examined many issues important for a practical recognition system.
{R}esults: {M}ost current discriminative methods for protein fold
prediction use the one-against-others method, which has the well-known
?{F}alse {P}ositives? problem. {W}e investigated two new methods:
the unique one-against-others and the all-against-all methods. {B}oth
improve prediction accuracy by 14?110% on a dataset containing 27
{SCOP} folds. {W}e used the {S}upport {V}ector {M}achine ({SVM})
and the {N}eural {N}etwork ({NN}) learning methods as base classifiers.
{SVM}s converges fast and leads to high accuracy. {W}hen scores of
multiple parameter datasets are combined, majority voting reduces
noise and increases recognition accuracy. {W}e examined many issues
involved with large number of classes, including dependencies of
prediction accuracy on the number of folds and on the number of representatives
in a fold. {O}verall, recognition systems achieve 56% fold prediction
accuracy on a protein test dataset, where most of the proteins have
below 25% sequence identity with the proteins used in training. {S}upplementary
information: {T}he protein parameter datasets used in this paper
are available online (http://www.nersc.gov/~cding/protein).},
pdf = {../local/Ding2001Multi-class.pdf},
file = {Ding2001Multi-class.pdf:local/Ding2001Multi-class.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/17/4/349.pdf}
}
@article{Guyon2002Gene,
author = {Guyon, I. and Weston, J. and Barnhill, S. and Vapnik, V.},
title = {Gene selection for cancer classification using support vector machines},
journal = {Mach. Learn.},
year = {2002},
volume = {46},
pages = {389-422},
number = {1/3},
month = {Jan},
abstract = {D{NA} micro-arrays now permit scientists to screen thousands of genes
simultaneously and determine whether those genes are active, hyperactive
or silent in normal or cancerous tissue. {B}ecause these new micro-array
devices generate bewildering amounts of raw data, new analytical
methods must be developed to sort out whether cancer tissues have
distinctive signatures of gene expression over normal tissues or
other types of cancer tissues. {I}n this paper, we address the problem
of selection of a small subset of genes from broad patterns of gene
expression data, recorded on {DNA} micro-arrays. {U}sing available
training examples from cancer and normal patients, we build a classifier
suitable for genetic diagnosis, as well as drug discovery. {P}revious
attempts to address this problem select genes with correlation techniques.
{W}e propose a new method of gene selection utilizing {S}upport {V}ector
{M}achine methods based on {R}ecursive {F}eature {E}limination ({RFE}).
{W}e demonstrate experimentally that the genes selected by our techniques
yield better classification performance and are biologically relevant
to cancer. {I}n contrast with the baseline method, our method eliminates
gene redundancy automatically and yields better and more compact
gene subsets. {I}n patients with leukemia our method discovered 2
genes that yield zero leave-one-out error, while 64 genes are necessary
for the baseline method to get the best result (one leave-one-out
error). {I}n the colon cancer database, using only 4 genes our method
is 98% accurate, while the baseline method is only 86% accurate.},
pdf = {../local/Guyon2002Gene.pdf},
file = {Guyon2002Gene.pdf:local/Guyon2002Gene.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://homepages.nyu.edu/~jaw281/genesel.pdf}
}
@article{Gartner2003Survey,
author = {G{\"a}rtner, T.},
title = {A {S}urvey of {K}ernels for {S}tructured {D}ata},
journal = {SIGKDD Explor. Newsl.},
year = {2003},
volume = {5},
pages = {49-58},
number = {1},
doi = {http://doi.acm.org/10.1145/959242.959248},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09}
}
@techreport{Haussler1999Convolution,
author = {Haussler, D.},
title = {Convolution {K}ernels on {D}iscrete {S}tructures},
institution = {UC Santa Cruz},
year = {1999},
number = {UCSC-CRL-99-10},
abstract = {We introduce a new method of constructing kernels on sets whose elements
are discrete structures like strings, trees and graphs. {T}he method
can be applied iteratively to build a kernel on a infinite set from
kernels involving generators of the set. {T}he family of kernels
generated generalizes the family of radial basis kernels. {I}t can
also be used to define kernels in the form of joint {G}ibbs probability
distributions. {K}ernels can be built from hidden {M}arkov random
fields, generalized regular expressions, pair-{HMM}s, or {ANOVA}
decompositions. {U}ses of the method lead to open problems involving
the theory of infinitely divisible positive definite functions. {F}undamentals
of this theory and the theory of reproducing kernel {H}ilbert spaces
are reviewed and applied in establishing the validity of the method.},
pdf = {../local/Haussler1999Convolution.pdf},
file = {Haussler1999Convolution.pdf:local/Haussler1999Convolution.pdf:PDF},
keywords = {biosvm},
subject = {kernel}
}
@inproceedings{Horvath2004Cyclic,
author = {T. Horv{\'a}th and T. G{\"a}rtner and S. Wrobel},
title = {Cyclic pattern kernels for predictive graph mining},
booktitle = {Proceedings of the tenth ACM SIGKDD international conference on Knowledge
discovery and data mining},
year = {2004},
pages = {158-167},
address = {New York, NY, USA},
publisher = {ACM Press},
doi = {http://doi.acm.org/10.1145/1014052.1014072},
keywords = {chemoinformatics kernel-theory},
owner = {mahe},
timestamp = {2006.08.02}
}
@article{Hua2001Novel,
author = {Hua, S. and Sun, Z.},
title = {A {N}ovel {M}ethod of {P}rotein {S}econdary {S}tructure {P}rediction
with {H}igh {S}egment {O}verlap {M}easure: {S}upport {V}ector {M}achine
{A}pproach},
journal = {J. {M}ol. {B}iol.},
year = {2001},
volume = {308},
pages = {397--407},
number = {2},
month = {April},
doi = {10.1006/jmbi.2001.4580},
pdf = {../local/Hua2001Novel.pdf},
file = {Hua2001Novel.pdf:local/Hua2001Novel.pdf:PDF},
keywords = {biosvm},
subject = {biokernel}
}
@article{Jaakkola2000Discriminative,
author = {Jaakkola, T. and Diekhans, M. and Haussler, D.},
title = {A {D}iscriminative {F}ramework for {D}etecting {R}emote {P}rotein
{H}omologies},
journal = {J. {C}omput. {B}iol.},
year = {2000},
volume = {7},
pages = {95--114},
number = {1,2},
pdf = {../local/jaak00.pdf},
file = {jaak00.pdf:local/jaak00.pdf:PDF},
keywords = {biosvm},
subject = {biokernelcasp},
url = {http://www.cse.ucsc.edu/research/compbio/discriminative/Jaakola2-1998.ps}
}
@inproceedings{Jaakkola1999Exploiting,
author = {Jaakkola, T. S. and Haussler, D.},
title = {Exploiting generative models in discriminative classifiers},
booktitle = {Proc. of {T}enth {C}onference on {A}dvances in {N}eural {I}nformation
{P}rocessing {S}ystems},
year = {1999},
pdf = {../local/jaak99.pdf},
file = {jaak99.pdf:local/jaak99.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.cse.ucsc.edu/research/ml/papers/Jaakola.ps}
}
@inproceedings{Jaakkola1999Probabilistic,
author = {Jaakkola, T. S. and Haussler, D.},
title = {Probabilistic kernel regression models},
booktitle = {Proceedings of the 1999 {C}onference on {AI} and {S}tatistics},
year = {1999},
publisher = {Morgan Kaufmann},
pdf = {../local/jaak99b.pdf},
file = {jaak99b.pdf:local/jaak99b.pdf:PDF},
subject = {kernel},
url = {http://alpha-bits.ai.mit.edu/people/tommi/publications/probker.ps.gz}
}
@article{Jebara2004Probability,
author = {Jebara, T. and Kondor, R. and Howard, A.},
title = {Probability {P}roduct {K}ernels},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2004},
volume = {5},
pages = {819-844},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v5/jebara04a.html}
}
@techreport{Kandola2002On,
author = {Kandola, J. and Shawe-Taylor, J. and Cristianini, N.},
title = {On the application of diffusion kernel to text data},
institution = {Neurocolt},
year = {2002},
note = {NeuroCOLT Technical Report NC-TR-02-122},
pdf = {../local/kand02.ps.gz},
file = {kand02.ps.gz:local/kand02.ps.gz:PostScript},
subject = {kernel},
url = {http://www.neurocolt.com/abs/2002/abs02122.html}
}
@article{Karchin2002Classifying,
author = {Karchin, R. and Karplus, K. and Haussler, D.},
title = {Classifying {G}-protein coupled receptors with support vector machines},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {147--159},
abstract = {Motivation: {T}he enormous amount of protein sequence data uncovered
by genome research has increased the demand for computer software
that can automate the recognition of new proteins. {W}e discuss the
relative merits of various automated methods for recognizing {G}-{P}rotein
{C}oupled {R}eceptors ({GPCR}s), a superfamily of cell membrane proteins.
{GPCR}s are found in a wide range of organisms and are central to
a cellular signalling network that regulates many basic physiological
processes. {T}hey are the focus of a significant amount of current
pharmaceutical research because they play a key role in many diseases.
{H}owever, their tertiary structures remain largely unsolved. {T}he
methods described in this paper use only primary sequence information
to make their predictions. {W}e compare a simple nearest neighbor
approach ({BLAST}), methods based on multiple alignments generated
by a statistical profile {H}idden {M}arkov {M}odel ({HMM}), and methods,
including {S}upport {V}ector {M}achines ({SVM}s), that transform
protein sequences into fixed-length feature vectors. {R}esults: {T}he
last is the most computationally expensive method, but our experiments
show that, for those interested in annotation-quality classification,
the results are worth the effort. {I}n two-fold cross-validation
experiments testing recognition of {GPCR} subfamilies that bind a
specific ligand (such as a histamine molecule), the errors per sequence
at the {M}inimum {E}rror {P}oint ({MEP}) were 13.7% for multi-class
{SVM}s, 17.1% for our {SVM}tree method of hierarchical multi-class
{SVM} classification, 25.5% for {BLAST}, 30% for profile {HMM}s,
and 49% for classification based on nearest neighbor feature vector
{K}ernel {N}earest {N}eighbor (kern{NN}). {T}he percentage of true
positives recognized before the first false positive was 65% for
both {SVM} methods, 13% for {BLAST}, 5% for profile {HMM}s and 4%
for kern{NN}. {A}vailability: {W}e have set up a web server for {GPCR}
subfamily classification based on hierarchical multi-class {SVM}s
at http://www.soe.ucsc.edu/research/compbio/gpcr-subclass. {B}y scanning
predicted peptides found in the human genome with the {SVM}tree server,
we have identified a large number of genes that encode {GPCR}s. {A}
list of our predictions for human {GPCR}s is available at http://www.soe.ucsc.edu/research/compbio/gpcr·hg/class·results.
{W}e also provide suggested subfamily classification for 18 sequences
previously identified as unclassified {C}lass {A} (rhodopsin-like)
{GPCR}s in {GPCRDB} ({H}orn et al. , {N}ucleic {A}cids {R}es. , 26,
277?281, 1998), available at http://www.soe.ucsc.edu/research/compbio/gpcr/class{A}·unclassified/},
comment = {Un papier intéressant sur l'utilisation du Fisher kernel pour classer
les GPCR, une famille de protéines importante pour l'industrie pharmaceutique.},
pdf = {../local/Karchin2002Classifying.pdf},
file = {Karchin2002Classifying.pdf:local/Karchin2002Classifying.pdf:PDF},
keywords = {fisher-kernel sequence-classification biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/reprint/18/1/147}
}
@inproceedings{Kim2008Robust,
author = {Kim, J. S. and Scott, C.},
title = {Robust kernel density estimation},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing ICASSP
2008},
year = {2008},
pages = {3381--3384},
doi = {10.1109/ICASSP.2008.4518376},
pdf = {../local/Kim2008Robust.pdf},
file = {Kim2008Robust.pdf:Kim2008Robust.pdf:PDF},
keywords = {kernelbook},
owner = {jp},
timestamp = {2011.07.23},
url = {http://dx.doi.org/10.1109/ICASSP.2008.4518376}
}
@inproceedings{Kondor2002Diffusion,
author = {R. I. Kondor and J. Lafferty},
title = {Diffusion kernels on graphs and other discrete input},
booktitle = {Proceedings of the Nineteenth International Conference on Machine
Learning},
year = {2002},
pages = {315--322},
address = {San Francisco, CA, USA},
publisher = {Morgan Kaufmann Publishers Inc.},
pdf = {../local/Kondor2002Diffusion.pdf},
file = {Kondor2002Diffusion.pdf:Kondor2002Diffusion.pdf:PDF},
keywords = {biosvm},
subject = {kernelnet}
}
@article{Lai2000Kernel,
author = {P.L. Lai and C. Fyfe},
title = {Kernel and nonlinear canonical correlation analysis},
journal = {Int. {J}. {N}eural {S}yst.},
year = {2000},
volume = {10},
pages = {365--377},
number = {5},
pdf = {../local/lai00.pdf},
file = {lai00.pdf:local/lai00.pdf:PDF},
subject = {kernel},
url = {http://www.worldscinet.com/journals/ijns/10/sample/S012906570000034X.html}
}
@article{Lanckriet2004Learning,
author = {Lanckriet, G.R.G. and Cristianini, N. and Bartlett, P. and El Ghaoui,
L. and Jordan, M.I.},
title = {Learning the kernel matrix with semidefinite programming},
journal = {J. Mach. Learn. Res.},
year = {2004},
volume = {5},
pages = {27-72},
pdf = {../local/Lanckriet2004Learning.pdf},
file = {Lanckriet2004Learning.pdf:Lanckriet2004Learning.pdf:PDF},
owner = {vert},
subject = {kernel},
url = {http://www.jmlr.org/papers/v5/lanckriet04a.html}
}
@inproceedings{Leslie2002spectrum,
author = {Leslie, C. and Eskin, E. and Noble, W.S.},
title = {The spectrum kernel: a string kernel for {SVM} protein classification},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {564--575},
address = {Singapore},
publisher = {World Scientific},
pdf = {../local/lesl02.pdf},
file = {lesl02.pdf:local/lesl02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel}
}
@inproceedings{Leslie2003Mismatch,
author = {Leslie, C. and Eskin, E. and Weston, J. and Noble, W.S.},
title = {Mismatch {S}tring {K}ernels for {SVM} {P}rotein {C}lassification},
booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 15},
year = {2003},
editor = {Suzanna Becker and Sebastian Thrun and Klaus Obermayer},
publisher = {MIT Press},
pdf = {../local/lesl02b.pdf},
file = {lesl02b.pdf:local/lesl02b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.cs.columbia.edu/~cleslie/papers/mismatch-short.pdf}
}
@inproceedings{Liao2002Combining,
author = {Liao, L. and Noble, W. S.},
title = {Combining pairwise sequence similarity and support vector machines
for remote protein homology detection},
booktitle = {Proceedings of the {S}ixth {I}nternational {C}onference on {C}omputational
{M}olecular {B}iology},
year = {2002},
pdf = {../local/liao02.pdf},
file = {liao02.pdf:local/liao02.pdf:PDF},
keywords = {biosvm},
subject = {biokernelcasp},
url = {http://www.cs.columbia.edu/~bgrundy/papers/fps-svm.html}
}
@article{Lodhi2002Text,
author = {Lodhi, H. and Saunders, C. and Shawe-Taylor, J. and Cristianini,
N. and Watkins, C.je n'ai pas vraiment d'éléments de réponse.},
title = {Text classification using string kernels},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2002},
volume = {2},
pages = {419--444},
pdf = {../local/lodh02.pdf},
file = {lodh02.pdf:local/lodh02.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.ai.mit.edu/projects/jmlr/papers/volume2/lodhi02a/abstract.html}
}
@inproceedings{Lodhi2000Text,
author = {Lodhi, H. and Shawe-Taylor, J. and Cristianini, N. and Watkins, C.
J. C. H.},
title = {Text {C}lassification using {S}tring {K}ernels},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2000},
pages = {563-569},
pdf = {../local/lodh00.pdf},
file = {lodh00.pdf:local/lodh00.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.neurocolt.com/tech_reps/2000/00079.ps.gz}
}
@techreport{Mahe2006pharmacophorea,
author = {P. Mah\'{e} and L. Ralaivola and V. Stoven and J.-P. Vert},
title = {The pharmacophore kernel for virtual screening with support vector
machines},
institution = {Ecole des {M}ines de {P}aris},
year = {2006},
number = {Technical Report HAL:ccsd-00020066},
month = {march},
keywords = {chemoinformatics kernel-theory},
owner = {mahe},
timestamp = {2006.07.31},
url = {http://hal.ccsd.cnrs.fr/ccsd-00020066}
}
@techreport{Mahe2006Graph,
author = {P. Mah{\'e} and J.-P. Vert},
title = {Graph kernels based on tree patterns for molecules},
institution = {HAL},
year = {2006},
number = {ccsd-00095488},
month = {September},
keywords = {chemoinformatics kernel-theory},
location = {Mines ParisTech},
owner = {mahe},
timestamp = {2006.10.10},
url = {https://hal.ccsd.cnrs.fr/ccsd-00095488}
}
@inproceedings{Mika1999Fisher,
author = {S. Mika and G. R{\"a}tsch and J. Weston and B. Sch{\"o}lkopf and
K.R. M{\"u}ller},
title = {Fisher discriminant analysis with kernels},
booktitle = {Neural {N}etworks for {S}ignal {P}rocessing {IX}},
year = {1999},
editor = {Y.-H. Hu and J. Larsen and E. Wilson and S. Douglas},
pages = {41--48},
publisher = {IEEE},
pdf = {../local/mika99.pdf},
file = {mika99.pdf:local/mika99.pdf:PDF},
subject = {kernel},
url = {http://ida.first.gmd.de/~mika/PS/MikRaeWesSchMue99.ps}
}
@techreport{Mukherjee1998Support,
author = {S. Mukherjee and P. Tamayo and J. P. Mesirov and D. Slonim and A.
Verri and T. Poggio},
title = {Support vector machine classification of microarray data},
institution = {C.B.L.C.},
year = {1998},
number = {182},
note = {A.I. Memo 1677},
pdf = {../local/Mukherjee1998Support.pdf},
file = {Mukherjee1998Support.pdf:local/Mukherjee1998Support.pdf:PDF},
keywords = {biosvm microarray},
subject = {biokernel},
url = {http://citeseer.nj.nec.com/437379.html}
}
@inproceedings{Patterson2002Pre-mRNA,
author = {Patterson, D.J. and Yasuhara, K. and Ruzzo, W.L.},
title = {Pre-{m{RNA}} secondary structure prediction aids splice site prediction.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {223-234},
publisher = {World Scientific},
abstract = {Accurate splice site prediction is a critical component of any computational
approach to gene prediction in higher organisms. {E}xisting approaches
generally use sequence-based models that capture local dependencies
among nucleotides in a small window around the splice site. {W}e
present evidence that computationally predicted secondary structure
of moderate length pre-m{RNA} subsequencies contains information
that can be exploited to improve acceptor splice site prediction
beyond that possible with conventional sequence-based approaches.
{B}oth decision tree and support vector machine classifiers, using
folding energy and structure metrics characterizing helix formation
near the splice site, achieve a 5-10% reduction in error rate with
a human data set. {B}ased on our data, we hypothesize that acceptors
preferentially exhibit short helices at the splice site.},
pdf = {../local/Patterson2002Pre-mRNA.pdf},
file = {Patterson2002Pre-mRNA.pdf:local/Patterson2002Pre-mRNA.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/patterson.pdf}
}
@inproceedings{Pavlidis2001Promoter,
author = {P. Pavlidis and T. S. Furey and M. Liberto and D. Haussler and W.
N. Grundy},
title = {Promoter {R}egion-{B}ased {C}lassification of {G}enes},
booktitle = {Pacific {S}ymposium on {B}iocomputing},
year = {2001},
pages = {139--150},
pdf = {../local/pavl01b.pdf},
file = {pavl01b.pdf:local/pavl01b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb01/pavlidis.pdf}
}
@inproceedings{Pavlidis2001Gene,
author = {Pavlidis, P. and Weston, J. and Cai, J. and Grundy, W.N.},
title = {Gene functional classification from heterogeneous data},
booktitle = {Proceedings of the {F}ifth {A}nnual {I}nternational {C}onference
on {C}omputational {B}iology},
year = {2001},
pages = {249--255},
pdf = {../local/pavl01.pdf},
file = {pavl01.pdf:local/pavl01.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.cs.columbia.edu/compbio/papers/exp-phylo.pdf}
}
@inproceedings{Platt1999Fast,
author = {J. Platt},
title = {Fast Training of Support Vector Machines using Sequential Minimal
Optimization},
booktitle = {Advances in Kernel Methods - Support Vector Learning},
year = {1999},
editor = {B. Schölkopf and C. Burges and A. Smola},
pages = {185-208},
publisher = {MIT Press, Cambridge, MA, USA},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.31}
}
@inproceedings{Ramon2003Expressivity,
author = {Ramon, J. and G\"{a}rtner, T.},
title = {{E}xpressivity versus efficiency of graph kernels},
booktitle = {{P}roceedings of the {F}irst {I}nternational {W}orkshop on {M}ining
{G}raphs, {T}rees and {S}equences},
year = {2003},
editor = {Washio, T. and De Raedt, L.},
pages = {65-74},
keywords = {kernel-theory chemoinformatics},
owner = {mahe},
timestamp = {2006.07.31}
}
@book{Saitoh1988Theory,
title = {Theory of reproducing {K}ernels and its applications},
publisher = {Longman Scientific \& Technical},
year = {1988},
author = {S. Saitoh},
address = {Harlow, UK},
subject = {kernel}
}
@incollection{Schoelkopf1999Kernel,
author = {Sch{\"o}lkopf, B. and Smola, A.J. and M{\"u}ller, K.-R.},
title = {Kernel principal component analysis},
booktitle = {Advances in {K}ernel {M}ethods - {S}upport {V}ector {L}earning},
publisher = {MIT Press},
year = {1999},
editor = {B. Sch{\"o}lkopf and C. Burges and A. Smola},
pages = {327--352},
pdf = {../local/scho99.pdf},
file = {scho99.pdf:local/scho99.pdf:PDF},
subject = {kernel}
}
@book{Scholkopf2002Learning,
title = {Learning with {K}ernels: {S}upport {V}ector {M}achines, {R}egularization,
{O}ptimization, and {B}eyond},
publisher = {MIT Press},
year = {2002},
author = {Sch{\"o}lkopf, B. and Smola, A. J.},
address = {Cambridge, MA},
subject = {kernel},
url = {http://www.learning-with-kernels.org}
}
@inproceedings{Schoelkopf2002Kernel,
author = {Sch{\"o}lkopf, B. and Weston, J. and Eskin, E. and Leslie, C. and
Noble, W.S.},
title = {A {K}ernel {A}pproach for {L}earning from {A}lmost {O}rthogonal {P}atterns},
booktitle = {Proceedings of {ECML} 2002},
year = {2002},
pdf = {../local/scho02.pdf},
file = {scho02.pdf:local/scho02.pdf:PDF},
subject = {biokernel},
url = {http://www.cs.columbia.edu/~cleslie/papers/domdiag.pdf}
}
@inproceedings{Scholkopf2000Support,
author = {Sch{\"o}lkopf, B. and Williamson, R. and Smola, A. and Shawe-Taylor,
J. and Platt, J.},
title = {Support {V}ector {M}ethod for {N}ovelty {D}etection},
booktitle = {Adv. {N}eural {I}nform. {P}rocess. {S}yst.},
year = {2000},
editor = {S.A. Solla and T.K. Leen and K.-R. M{\"u}ller},
volume = {12},
pages = {582--588},
publisher = {MIT Press},
pdf = {../local/scho99.pdf},
file = {scho99.pdf:local/scho99.pdf:PDF},
subject = {kernel},
url = {http://citeseer.nj.nec.com/400144.html}
}
@inproceedings{Stapley2002Predicting,
author = {Stapley, B.J. and Kelley, L.A. and Sternberg, M.J.},
title = {Predicting the sub-cellular location of proteins from text using
support vector machines.},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Kevin
Lauerdale and Teri E. Klein},
pages = {374-385},
publisher = {World Scientific},
abstract = {We present an automatic method to classify the sub-cellular location
of proteins based on the text of relevant medline abstracts. {F}or
each protein, a vector of terms is generated from medline abstracts
in which the protein/gene's name or synonym occurs. {A} {S}upport
{V}ector {M}achine ({SVM}) is used to automatically partition the
term space and to thus discriminate the textual features that define
sub-cellular location. {T}he method is benchmarked on a set of proteins
of known sub-cellular location from {S}. cerevisiae. {N}o prior knowledge
of the problem domain nor any natural language processing is used
at any stage. {T}he method out-performs support vector machines trained
on amino acid composition and has comparable performance to rule-based
text classifiers. {C}ombining text with protein amino-acid composition
improves recall for some sub-cellular locations. {W}e discuss the
generality of the method and its potential application to a variety
of biological classification problems.},
pdf = {../local/Stapley2002Predicting.pdf},
file = {Stapley2002Predicting.pdf:local/Stapley2002Predicting.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/stapley.pdf}
}
@article{Tsuda2004Learning,
author = {Tsuda, K. and Noble, W.S.},
title = {Learning kernels from biological networks by maximizing entropy},
journal = {Bioinformatics},
year = {2004},
volume = {20},
pages = {i326--i333},
abstract = {Motivation: {T}he diffusion kernel is a general method for computing
pairwise distances among all nodes in a graph, based on the sum of
weighted paths between each pair of nodes. {T}his technique has been
used successfully, in conjunction with kernel-based learning methods,
to draw inferences from several types of biological networks. {R}esults:
{W}e show that computing the diffusion kernel is equivalent to maximizing
the von {N}eumann entropy, subject to a global constraint on the
sum of the {E}uclidean distances between nodes. {T}his global constraint
allows for high variance in the pairwise distances. {A}ccordingly,
we propose an alternative, locally constrained diffusion kernel,
and we demonstrate that the resulting kernel allows for more accurate
support vector machine prediction of protein functional classifications
from metabolic and protein?protein interaction networks. {A}vailability:
{S}upplementary results and data are available at noble.gs.washington.edu/proj/maxent},
comment = {Problem = multiclass classification of tumor cells from gene expression.
Show that the one-versus-all approach of combining SVM yields the
minimum number of classification errors on their Affymetrix data
with 14 tumor types. In addition to not taking variability estimates
of repeated measurements into account, this approach selects different
relevant features (genes) for each binary classifier.},
doi = {10.1093/bioinformatics/bth906},
pdf = {../local/Tsuda2004Learning.pdf},
file = {Tsuda2004Learning.pdf:local/Tsuda2004Learning.pdf:PDF},
keywords = {learning-kernel graph-kernel biosvm},
owner = {vert},
url = {http://dx.doi.org/10.1093/bioinformatics/bth906}
}
@book{Vapnik1998Statistical,
title = {Statistical {L}earning {T}heory},
publisher = {Wiley},
year = {1998},
author = {Vapnik, V. N.},
address = {New-York},
subject = {kernel}
}
@inproceedings{Vert2002Support,
author = {Vert, J.-P.},
title = {Support vector machine prediction of signal peptide cleavage site
using a new class of kernels for strings},
booktitle = {Proceedings of the {P}acific {S}ymposium on {B}iocomputing 2002},
year = {2002},
editor = {R. B. Altman and A. K. Dunker and L. Hunter and K. Lauerdale and
T. E. Klein},
pages = {649--660},
publisher = {World Scientific},
pdf = {../local/vert02.pdf},
file = {vert02.pdf:local/vert02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://www.smi.stanford.edu/projects/helix/psb02/vert.pdf}
}
@article{Vert2002tree,
author = {Vert, J.-P.},
title = {A tree kernel to analyze phylogenetic profiles},
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {S276--S284},
pdf = {../local/vert02b.pdf},
file = {vert02b.pdf:local/vert02b.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://cbio.ensmp.fr/~jvert/publi/ismb02/index.html}
}
@techreport{Vinokourov2002Finding,
author = {Vinokourov, A. and Shawe-Taylor, J. and Cristianini, N.},
title = {Finding {L}anguage-{I}ndependent {S}emantic {R}epresentation of {T}ext
using {K}ernel {C}anonical {C}orrelation {A}nalysis},
institution = {Neurocolt},
year = {2002},
note = {NeuroCOLT Technical Report NC-TR-02-119},
pdf = {../local/vino02.ps.gz},
file = {vino02.ps.gz:local/vino02.ps.gz:PostScript},
subject = {kernel},
url = {http://www.neurocolt.com/abs/2002/abs02119.html}
}
@incollection{Watkins2000Dynamic,
author = {C. Watkins},
title = {Dynamic alignment kernels},
booktitle = {Advances in {L}arge {M}argin {C}lassifiers},
publisher = {MIT Press},
year = {2000},
editor = {A.J. Smola and P.L. Bartlett and B. Sch{\"o}lkopf and D. Schuurmans},
pages = {39--50},
address = {Cambridge, MA},
pdf = {../local/Watkins2000Dynamic.pdf},
file = {Watkins2000Dynamic.pdf:local/Watkins2000Dynamic.pdf:PDF},
keywords = {biosvm},
subject = {kernel},
url = {http://www.cs.rhbnc.ac.uk/home/chrisw/dynk.ps.gz}
}
@article{Wolf2003Learning,
author = {Wolf, L. and Shashua, A.},
title = {Learning over {S}ets using {K}ernel {P}rincipal {A}ngles},
journal = {J. {M}ach. {L}earn. {R}es.},
year = {2003},
volume = {4},
pages = {913-931},
keywords = {kernel-theory},
owner = {mahe},
timestamp = {2006.08.09},
url = {http://jmlr.csail.mit.edu/papers/v4/wolf03a.html}
}
@article{Zavaljevski2002Support,
author = {Zavaljevski, N. and Stevens, F.J. and Reifman, J.},
title = {Support vector machines with selective kernel scaling for protein
classification and identification of key amino acid positions },
journal = {Bioinformatics},
year = {2002},
volume = {18},
pages = {689--696},
number = {5},
abstract = {Motivation: {D}ata that characterize primary and tertiary structures
of proteins are now accumulating at a rapid and accelerating rate
and require automated computational tools to extract critical information
relating amino acid changes with the spectrum of functionally attributes
exhibited by a protein. {W}e propose that immunoglobulin-type beta-domains,
which are found in approximate 400 functionally distinct forms in
humans alone, provide the immense genetic variation within limited
conformational changes that might facilitate the development of new
computational tools. {A}s an initial step, we describe here an approach
based on {S}upport {V}ector {M}achine ({SVM}) technology to identify
amino acid variations that contribute to the functional attribute
of pathological self-assembly by some human antibody light chains
produced during plasma cell diseases. {R}esults: {W}e demonstrate
that {SVM}s with selective kernel scaling are an effective tool in
discriminating between benign and pathologic human immunoglobulin
light chains. {I}nitial results compare favorably against manual
classification performed by experts and indicate the capability of
{SVM}s to capture the underlying structure of the data. {T}he data
set consists of 70 proteins of human antibody 1 light chains, each
represented by aligned sequences of 120 amino acids. {W}e perform
feature selection based on a first-order adaptive scaling algorithm,
which confirms the importance of changes in certain amino acid positions
and identifies other positions that are key in the characterization
of protein function.},
pdf = {../local/zava02.pdf},
file = {zava02.pdf:local/zava02.pdf:PDF},
keywords = {biosvm},
subject = {biokernel},
url = {http://bioinformatics.oupjournals.org/cgi/content/abstract/18/5/689}
}
@comment{{jabref-meta: selector_author:}}
@comment{{jabref-meta: selector_journal:Adv. Drug Deliv. Rev.;Am. J. Hu
m. Genet.;Am. J. Pathol.;Ann. Appl. Stat.;Ann. Math. Statist.;Ann. N.
Y. Acad. Sci.;Ann. Probab.;Ann. Stat.;Artif. Intell. Med.;Bernoulli;Bi
ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
r. Comput. Sci.;Trans. Am. Math. Soc.;Trends Genet.;}}
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