CLAIR Group Library

Books

Name	Author	Holder	Date
A Probability Theory of Pattern Recognition [Google...]	Devroye, Gyorfi and Lugosi	----	----
Statistical Learning Theory [Google...]	Vladimir N. Vapnik	----	----
Introduction to Protozoic	Daniel Liebler	----	----
Introduction to Proteomics	Daniel Liebler	----	----
An Introduction to Multivariate Statistical Analysis (3rd ed.)	T. W. Anderson	----	----
Linear and Nonlinear Programming (2nd ed.)	David Luenberger	----	----
Statistical Decision Theory	James O. Berger	----	----
Time Series Analysis	James D. Hamilton	Sachin	07/17/07
Independent Component Analysis	Aapo Hyvarinen, Juha Karhunen, and Erkki Oja	Abhi	08/30
Convex Optimization	Stephen Boyd and Lieven Vandenberghe	----	----
Kernel Methods for Pattern Analysis	John Shawe-Taylor and Nello Cristianini	Abhi	08/30
Molecular Biology made simple and fun	David P. Clark and Lonnie D. Russell	Yiming	05/12/2004
The Cartoon Guide to Genetics	Larry Gonick and Mark Wheelis	----	----
Computer-Aided Multivariate Analysis	A.A. Afifi and V. Clark	----	----
Nonparametric Statistical Methods (2nd edition)	Myles Hollander and Douglas A. Wolfe	----	----
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods	Nello Cristianini and John Shawe-Taylor	Yiming	----
25 Years of SIGIR Proceedings: 1978-2002	ACM SIGIR	----	----
Principles of Mathematical Analysis	Walter Rudin	----	----
Real and Complex Analysis	Walter Rudin	----	----
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond	Bernhard Scholkopf and Alexander J. Smola
Practical Methods of Optimization	Roger Fletcher	----	----
EuroWordNet: A multilingual Database with Lexical Semantic Networks	Piek Vossen (editor)	Abhi	06/29/2006
Cross-Language Information Retrieval	Gregory Grefenstette (editor)	Abhi	06/30/2006
Pattern Classification(2nd Edition)	Richard O. Duda, Peter E. Hart, David G. Stork	----	----
Elements of Information Theory	Thomas M. Cover, Joy A. Thomas	Abhi	07/27/2006
The Elements of Statistical Learning (Data Mining, Inference, and Prediction)	Trevor Hastie, Robert Tibshirani, Jerome Friedman	Suresh	08/27/06
Multivariate Density Estimation: Theory, Practice, and Visualization	David W. Scott	----	----
Learning in Graphical Models	Michael I. Jordan (editor)	----	----
Applied Linear Regression	Sanford Weisberg	Yiming	----
The Maximum-Margin Approach to Learning Text Classifiers: Methods, Theory and Algorithms	Thorsten Joachims (thesis)	----	----
Bioinformatics: Sequence and Genome Analysis	David W. Mount	Fan	10/03/2002
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids	R. Durbin, S. Eddy, A. Krogh and G. Mitchison	Fan	10/03/2002
Probability and Statistics (3rd Edition)	Morris H. Degroot and Mark J. Schervish
Proceedings of SIGIR 2002		----	----
Stochastic Processes (2nd edition)	Sheldon M. Ross	----	----
Probability and Measure Theory (2nd edition) [Google...]	Ash and Dade	----	----
Optimization by Vector Space Methods [Google...]	David G. Luenberger	Yiming	07/28/06
Statistical Inference [Reference]	Casella, Berger	---	---
Matrix Analysis and Applied Linear Algebra		---	---
Matrix Analysis and Applied Linear Algebra - Sol. Manual		---	---
Pattern Recognition and Machine Learning	Christopher Bishop	---	---
Modern Information Retrieval	Ricardo Yates	Abhay	02/07/2007
Introduction to Statistical Relational Learning [Google...]	Ed. Lise Getoor and Ben Taskar	Suresh	11/02/2007

Policies

How to borrow?

See whether the book is available. If not, coordinate with the current holder.
In any case, contact Abhi (alad [at] cs [dot] cmu [dot] edu) so that he can update the records.
Collect the book from the previous holder, or from the shelves in Yiming's office (3612B).

How long?

Procedure for ordering a new book:

Yiming (yiming [at] cs [dot] cmu [dot] edu)

NOT for class use

Abhi (alad [at] cs [dot] cmu [dot] edu

Book Introductions

Elements of Information Theory The most famous book about information theory, including but not limited to: three shannon theorems together with their applications in data compression, language, channel, stock market, etc.
Linear and nonlinear programming This book is a text covering the central concepts of practical optimization technique, which is one of the core techniques in machine learning. It is well written, and is one of the highly ranked books by citeseer computer science articles. Its content includes: simplex method, duality theorem, basic descent method, conjugate direction method, quasi-newton method, constrained optimization problem, primal and dual methods, lagrange method, etc.
Learning in Graphical Models Edited by Michael Jordan, this book includes many worth reading papers about graphic models, and what you can learn are far more beyond that: bayesian inference, variational method, monte carlo method, mean field learning, clustering, etc.
Bioinformatics: Sequence and Genome Analysis A good book introducing how to use computer science technique to process's proteins and DNAs, including sequence alignment, RNA secondary structure prediction, phylogenetic prediction, database searching for similar sequences, gene prediction, protein classification and structure prediction and genome analysis.
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids This book provides a overview of sequence analysis methods, with particular emphasis on probabilistic modeling. Pairwise alignment, hidden markov models, multiple alignment, profile search, RNA secondary structure analysis and phylogenetic inference.
The Maximum-Margin Approach to Learning Text Classifiers: Methods, Theory and Algorithms It first provide an overview to text classification, then introduces the principle of SVM classifier. The paper focused on the efficient implementation of SVM and its extensions. It also provided the experiment results which is a good baseline.
EuroWordNet: A Multilingual Database with Lexical Semantic Networks An introduction to multilingual lexical database that is similar to WordNet.
Cross-Language Information Retrieval The universal adoption of the Internet and the WWW have created an enormous, multilingual virtual textual database. Rather than looking upon foreign language documents as distracting noise, one can consider these documents as untapped sources of information. Cross-Language Information Retrieval is the first book that addresses the problem of accessing multilingual information through a single-language query. This research problem is receiving growing attention by US and foreign governments. Cross-Language Information Retrieval describes the problem, highlighting the differences between the field and the related areas of Machine Translation and Information Retrieval. Researchers from Europe, Japan and America present a wide variety of techniques and experimental results. The life-size experiments are run on modern large-scale retrieval testbeds, running up to hundreds of megabytes of texts. The techniques involve using bilingual dictionaries, machine translation systems, parallel text corpora, comparable but non-parallel text corpora, latent semantic indexing, and weighted Boolean interrogation. Cross-Language Information Retrieval is suitable as a secondary text for a graduate level course on Cross-Language Information Retrieval, and as a reference for researchers and practitioners in industry.
Probability and Statistics Probabilities and Statistics are hard to learn. Nevertheless, the author teaches you through an impressive quantity of examples. You don't need to be a genius in math's to understand him, because he explains pretty well. The equations are all understandable and the author's doesn't use I high level of sophistication to present complex problems. The content is pretty impressive; besides the classical probability theory , there is an extensive section dealing with estimation techniques (maximum of likelihood, OLS, and Bayesian estimators) there is a chapter dealing with statistical tests and another with non-parametric methods.

Errors/Suggestions/Other bookish issues - Mail Abhi (alad [at] cs [dot] cmu [dot] edu)

Last changed: 12/31/07 (Change Log)