Header logo is ei


2001


no image
An Introduction to Kernel-Based Learning Algorithms

Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.

IEEE Transactions on Neural Networks, 12(2):181-201, March 2001 (article)

Abstract
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis

DOI [BibTex]

2001

DOI [BibTex]


no image
Estimating the support of a high-dimensional distribution.

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.

Neural Computation, 13(7):1443-1471, March 2001 (article)

Abstract
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.

Web DOI [BibTex]

Web DOI [BibTex]


no image
The psychometric function: II. Bootstrap-based confidence intervals and sampling

Wichmann, F., Hill, N.

Perception and Psychophysics, 63 (8), pages: 1314-1329, 2001 (article)

PDF [BibTex]

PDF [BibTex]


no image
The psychometric function: I. Fitting, sampling and goodness-of-fit

Wichmann, F., Hill, N.

Perception and Psychophysics, 63 (8), pages: 1293-1313, 2001 (article)

Abstract
The psychometric function relates an observer'sperformance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function'sparameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (or lapses ). We show that failure to account for this can lead to serious biases in estimates of the psychometric function'sparameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditional X^2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods

PDF [BibTex]

PDF [BibTex]


no image
The control structure of artificial creatures

Zhou, D., Dai, R.

Artificial Life and Robotics, 5(3), 2001, invited article (article)

Web [BibTex]

Web [BibTex]


no image
Markovian domain fingerprinting: statistical segmentation of protein sequences

Bejerano, G., Seldin, Y., Margalit, H., Tishby, N.

Bioinformatics, 17(10):927-934, 2001 (article)

PDF Web [BibTex]

PDF Web [BibTex]