Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles

[1]  Dwijendra K. Ray-Chaudhuri,et al.  Binary mixture flow with free energy lattice Boltzmann methods , 2022, arXiv.org.

[2]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[3]  D. M. Weir,et al.  Handbook of experimental immunology , 1967 .

[4]  L. Breiman,et al.  Submodel selection and evaluation in regression. The X-random case , 1992 .

[5]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[6]  Harry Wechsler,et al.  From Statistics to Neural Networks , 1994, NATO ASI Series.

[7]  Jerome H. Friedman,et al.  An Overview of Predictive Learning and Function Approximation , 1994 .

[8]  Shlomo Nir,et al.  NATO ASI Series , 1995 .

[9]  Kishan G. Mehrotra,et al.  Efficient classification for multiclass problems using modular neural networks , 1995, IEEE Trans. Neural Networks.

[10]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[11]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[12]  Eddy Mayoraz,et al.  On the Decomposition of Polychotomies into Dichotomies , 1997, ICML.

[13]  Eddy Mayoraz,et al.  Improved Pairwise Coupling Classification with Correcting Classifiers , 1998, ECML.

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Vladimir Cherkassky,et al.  Learning from Data: Concepts, Theory, and Methods , 1998 .

[17]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[18]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[19]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[20]  J. Vose,et al.  Current approaches to the management of non-Hodgkin's lymphoma. , 1998, Seminars in oncology.

[21]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[22]  Ron Shamir,et al.  Clustering Gene Expression Patterns , 1999, J. Comput. Biol..

[23]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Christian A. Rees,et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[25]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[26]  P. Brown,et al.  DNA arrays for analysis of gene expression. , 1999, Methods in enzymology.

[27]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[28]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[29]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Linda Kaufman,et al.  Solving the quadratic programming problem arising in support vector classification , 1999 .

[31]  L. Staudt,et al.  Regulation of lymphocyte cell fate decisions and lymphomagenesis by BCL-6. , 1999, International reviews of immunology.

[32]  Roded Sharan,et al.  Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis , 2000, ISMB.

[33]  Ash A. Alizadeh,et al.  Ongoing immunoglobulin somatic mutation in germinal center B cell-like but not in activated B cell-like diffuse large cell lymphomas. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Thomas G. Dietterich Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[35]  Trevor Hastie,et al.  Gene Shaving: a new class of clustering methods for expression arrays , 2000 .

[36]  E. Winzeler,et al.  Genomics, gene expression and DNA arrays , 2000, Nature.

[37]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[38]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[39]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[40]  Nello Cristianini,et al.  Margin Distribution and Soft Margin , 2000 .

[41]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[42]  Giorgio Valentini,et al.  Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems , 2000, Multiple Classifier Systems.

[43]  Giorgio Valentini,et al.  Parallel non-linear dichotomizers , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[44]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[45]  Thorsten Joachims,et al.  Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.

[46]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[47]  Nir Friedman,et al.  Tissue classification with gene expression profiles. , 2000 .

[48]  Pierre Baldi,et al.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..

[49]  Jason Weston,et al.  Gene functional classification from heterogeneous data , 2001, RECOMB.

[50]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[51]  Tin Kam Ho,et al.  Data Complexity Analysis for Classifier Combination , 2001, Multiple Classifier Systems.

[52]  Sayan Mukherjee,et al.  Molecular classification of multiple tumor types , 2001, ISMB.

[53]  Giorgio Valentini,et al.  Bias-Variance Analysis and Ensembles of SVM , 2002, Multiple Classifier Systems.

[54]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[55]  Giorgio Valentini,et al.  NEURObjects: an object-oriented library for neural network development , 2002, Neurocomputing.

[56]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.