CiteULike: stanley's library [24 articles]

LIBSVM: a library for support vector machines

Chih Chang — 2006-12-28T13:29:04-00:00

(2001)

Comparing naive Bayes, decision trees, and SVM with AUC and accuracy

J Huang — 2006-12-03T03:20:28-00:00

Data Mining, 2003. ICDM 2003. Third IEEE International Conference on (2003), pp. 553-556.

Predictive accuracy has often been used as the main and often only evaluation criterion for the predictive performance of classification or data mining algorithms. In recent years, the area under the ROC (receiver operating characteristics) curve, or simply AUC, has been proposed as an alternative single-number measure for evaluating performance of learning algorithms. We proved that AUC is, in general, a better measure (defined precisely) than accuracy. Many popular data mining algorithms should then be reevaluated in terms of AUC. For example, it is well accepted that Naive Bayes and decision trees are very similar in accuracy. How do they compare in AUC? Also, how does the recently developed SVM (support vector machine) compare to traditional learning algorithms in accuracy and AUC? We will answer these questions. Our conclusions will provide important guidelines in data mining applications on real-world datasets.

PEBL: Positive Example-Based Learning for Web Page Classification Using SVM

H Yu — 2006-11-29T08:18:54-00:00

(2002)

for Web mining. Specifically, classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web. However, constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples. For instance, in order to construct a "homepage" classifier, one needs to collect a sample of homepages (positive examples) and a sample of non-homepages (negative examples). In particular,...

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Ron Kohavi — 2006-03-05T17:32:28-00:00

(1995), pp. 1137-1145.

We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone -out cross-validation. We report on a largescale experiment---over half a million runs of C4.5 and a Naive-Bayes algorithm---to...

ROC Graphs: Notes and Practical Considerations for Researchers

T Fawcett — 2006-05-11T06:27:05-00:00

(2004)

Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a ...

Classification of non-coding rna using graph representations of secondary structure

YAN Karklin — 2006-02-03T10:06:26-00:00

(2005)

Some genes produce transcripts that function directly in regulatory, catalytic, or structural roles in the cell. These non-coding RNAs are prevalent in all living organisms, and methods that aid the understanding of their functional roles are essential. RNA secondary structure, the pattern of base-pairing, contains the critical information for determining the three dimensional structure and function of the molecule. In this work we examine whether the basic geometric and topological properties of secondary structure are sufficient to distinguish between RNA families in a learning framework. First, we develop a labeled dual graph representation of RNA secondary structure by adding biologically meaningful labels to the dual graphs proposed by Gan et al [1]. Next, we define a similarity measure directly on the labeled dual graphs using the recently developed marginalized kernels [2]. Using this similarity measure, we were able to train Support Vector Machine classifiers to distinguish RNAs of known families from random RNAs with similar statistics. For 22 of the 25 families tested, the classifier achieved better than 70% accuracy, with much higher accuracy rates for some families. Training a set of classifiers to automatically assign family labels to RNAs using a one vs. all multi-class scheme also yielded encouraging results. From these initial learning experiments, we suggest that the labeled dual graph representation, together with kernel machine methods, has potential for use in automated analysis and classification of uncharacterized RNA molecules or efficient genome-wide screens for RNA molecules from existing families.

Cluster Analysis and Data Visualization of Large-Scale Gene Expression Data

G Michaels — 2005-10-18T02:27:04-00:00

(1998)

Self-Organizing Maps

Teuvo Kohonen — 2005-05-10T06:12:32-00:00

(28 December 2000)

Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison

2005-10-18T00:55:07-00:00

(01 December 1999)

Time Warps, String Edits and Macromolecules is a young classic in computational science. The computational perspective is that of sequence processing, in particular the problem of recognizing related sequences. The book is the first, and still best compilation of papers explaining how to measure distance between sequences, and how to compute that measure effectively. This is called string distance, Levenshtein distance, or edit distance. The book contains lucid explanations of the basic techniques; well-annotated examples of applications; mathematical analysis of its computational (algorithmic) complexity; and extensive discussion of the variants needed for weighted measures, timed sequences (songs), applications to continuous data, comparison of multiple sequences and extensions to tree-structures. This theory finds applications in molecular biology, speech recognition, analysis of bird song and error correcting in computer software.

Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)

Gene Golub — 2005-07-12T18:58:24-00:00

(15 October 1996)

<P>Revised and updated, the third edition of Golub and Van Loan's classic text in computer science provides essential information about the mathematical background and algorithmic skills required for the production of numerical software. This new edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of the modified Gram-Schmidt process, and new material devoted to GMRES, QMR, and other methods designed to handle the sparse unsymmetric linear system problem.</P>

Singular Value Decomposition and Principal Component Analysis

Michael Wall — 2005-10-17T02:36:40-00:00

(3 Mar 2003), pp. 91-109.

This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.

Elements of Information Theory

Thomas Cover — 2005-08-31T13:39:17-00:00

(12 August 1991)

Following a brief introduction and overview, early chapters cover the basic algebraic relationships of entropy, relative entropy and mutual information, AEP, entropy rates of stochastics processes and data compression, duality of data compression and the growth rate of wealth. Later chapters explore Kolmogorov complexity, channel capacity, differential entropy, the capacity of the fundamental Gaussian channel, the relationship between information theory and statistics, rate distortion and network information theories. The final two chapters examine the stock market and inequalities in information theory. In many cases the authors actually describe the properties of the solutions before the presented problems.

Algorithms for Clustering Data (Prentice Hall Advanced Reference Series : Computer Science)

Anil Jain — 2005-10-13T00:54:42-00:00

(01 March 1988)

Computing the Edit-Distance between Unrooted Ordered Trees

P Klein — 2005-10-04T02:37:22-00:00

No. 1461. (1998), pp. 91-102.

. An ordered tree is a tree in which each node's incident edges are cyclically ordered; think of the tree as being embedded in the plane. Let A and B be two ordered trees. The edit distance between A and B is the minimum cost of a sequence of operations (contract an edge, uncontract an edge, modify the label of an edge) needed to transform A into B. We give an O(n 3 log n) algorithm to compute the edit distance between two ordered trees. 1 Introduction A tree is said to be ordered if...

Computing similarity between RNA structures

Bin Ma — 2005-10-04T02:07:07-00:00

Theor. Comput. Sci., Vol. 276, No. 1-2. (2002), pp. 111-132.

Applying discrete PCA in data analysis

Wray Buntine — 2005-10-04T00:50:41-00:00

(2004), pp. 59-66.

Data clustering: a review

AK Jain — 2004-12-14T01:51:02-00:00

ACM Computing Surveys, Vol. 31, No. 3. (1999), pp. 264-323.

This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information...

Fast Folding and Comparison of RNA Secondary Structures

Ivo Hofacker — 2005-10-03T07:14:42-00:00

Monatsh.\ Chem., Vol. 125 (1994), pp. 167-188.

Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions and base pairing probabilities. An efficient heuristic for the inverse folding problem of RNA is introduced. In addition we present compact and efficient programs for the comparison of RNA secondary...

A block-sorting lossless data compression algorithm.

M Burrows — 2005-10-03T04:55:51-00:00

No. 124. (1994)

We describe a block-sorting, lossless data compression algorithm, and our implementation of that algorithm. We compare the performance of our implementation with widely available data compressors running on the same hardware. The algorithm works by applying a reversible transformation to a block of input text. The transformation does not itself compress the data, but re-orders it to make it easy to compress with simple algorithms such as move-to-front encoding. Our algorithm achieves speed...

Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids

Richard Durbin — 2005-04-18T14:42:40-00:00

(01 July 1999)

Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.

Language Trees and Zipping

Dario Benedetto — 2005-09-09T06:35:45-00:00

Physical Review Letters, Vol. 88, No. 4. (2002)

In this Letter we present a very general method for extracting information from a generic string of characters, e.g., a text, a DNA sequence, or a time series. Based on data-compression techniques, its key point is the computation of a suitable measure of the remoteness of two bodies of knowledge. We present the implementation of the method to linguistic motivated problems, featuring highly accurate results for language recognition, authorship attribution, and language classification.

An Introduction to Kolmogorov Complexity and Its Applications (Texts in Computer Science)

Ming Li — 2005-09-08T02:25:34-00:00

(27 February 1997)

"The book is outstanding and admirable in many respects. ... is necessary reading for all kinds of readers from undergraduate students to top authorities in the field." Journal of Symbolic Logic Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and their applications of Kolmogorov complexity. the book presents a thorough treatment of the subject with a wide range of illsutrative applications. Such applications include the randomeness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computationla learning theory, the complexity of algorithms, and the thermodynamics of computing. It will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics. the book is self-contained in that it contains the basic requirements from mathematics and computer science. Included are also numerous problem sets, comments, source references, and himnts to solutions of problems. In this new edition the authors have added new material on circuit theory, distributed algorithms, data compression, and other topics.

Suicide Gene Therapy: Methods and Reviews (Methods in Molecular Medicine, 90)

Caroline Springer — 2005-06-17T00:30:40-00:00

(01 June 2004)

Viral Vectors for Gene Therapy: Methods and Protocols (Methods in Molecular Medicine, 76)

Curtis Machida — 2005-06-17T00:31:23-00:00

(01 November 2002)