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2002


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Support Vector Machines and Kernel Methods: The New Generation of Learning Machines

Cristianini, N., Schölkopf, B.

AI Magazine, 23(3):31-41, 2002 (article)

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[BibTex]

2002



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Contrast discrimination with pulse-trains in pink noise

Henning, G., Bird, C., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

Abstract
Detection performance was measured with sinusoidal and pulse-train gratings. Although the 2.09-c/deg pulse-train, or line gratings, contained at least 8 harmonics all at equal contrast, they were no more detectable than their most detectable component. The addition of broadband pink noise designed to equalize the detectability of the components of the pulse train made the pulse train about a factor of four more detectable than any of its components. However, in contrast-discrimination experiments, with a pedestal or masking grating of the same form and phase as the signal and 15% contrast, the noise did not affect the discrimination performance of the pulse train relative to that obtained with its sinusoidal components. We discuss the implications of these observations for models of early vision in particular the implications for possible sources of internal noise.

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PDF [BibTex]

PDF [BibTex]

2001


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Regularized principal manifolds

Smola, A., Mika, S., Schölkopf, B., Williamson, R.

Journal of Machine Learning Research, 1, pages: 179-209, June 2001 (article)

Abstract
Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised learning. This setting turns out to be closely related to principal curves, the generative topographic map, and robust coding. We explore this connection in two ways: (1) we propose an algorithm for finding principal manifolds that can be regularized in a variety of ways; and (2) we derive uniform convergence bounds and hence bounds on the learning rates of the algorithm. In particular, we give bounds on the covering numbers which allows us to obtain nearly optimal learning rates for certain types of regularization operators. Experimental results demonstrate the feasibility of the approach.

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PDF [BibTex]

2001


PDF [BibTex]


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The psychometric function: II. Bootstrap-based confidence intervals and sampling

Wichmann, F., Hill, N.

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

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PDF [BibTex]

PDF [BibTex]


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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

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PDF [BibTex]

PDF [BibTex]


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Markovian domain fingerprinting: statistical segmentation of protein sequences

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

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

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PDF Web [BibTex]

PDF Web [BibTex]

1998


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SVMs — a practical consequence of learning theory

Schölkopf, B.

IEEE Intelligent Systems and their Applications, 13(4):18-21, July 1998 (article)

Abstract
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

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PDF Web DOI [BibTex]

1998


PDF Web DOI [BibTex]


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Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.

Autonomous Robots, 5(1):111-125, March 1998 (article)

Abstract
We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.

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PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]