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31 results (BibTeX)

1999


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Kernel principal component analysis.

Schölkopf, B., Smola, A., Müller, K.

In Advances in Kernel Methods—Support Vector Learning, pages: 327-352, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

1999

[BibTex]


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Estimating the support of a high-dimensional distribution

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

(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

Web [BibTex]

Web [BibTex]


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Single-class Support Vector Machines

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

Dagstuhl-Seminar on Unsupervised Learning, pages: 19-20, (Editors: J. Buhmann, W. Maass, H. Ritter and N. Tishby), 1999 (poster)

[BibTex]

[BibTex]


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Some Aspects of Modelling Human Spatial Vision: Contrast Discrimination

Wichmann, F.

University of Oxford, University of Oxford, October 1999 (phdthesis)

[BibTex]

[BibTex]


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Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

Balakrishnan, K., Bousquet, O., Honavar, V.

Adaptive Behavior, 7(2):173-216, 1999 (article)

[BibTex]


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SVMs for Histogram Based Image Classification

Chapelle, O., Haffner, P., Vapnik, V.

IEEE Transactions on Neural Networks, (9), 1999 (article)

Abstract
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form $K(mathbf{x},mathbf{y})=e^{-rhosum_i |x_i^a-y_i^a|^{b}}$ with $aleq 1$ and $b leq 2$ are evaluated on the classification of images extracted from the Corel Stock Photo Collection and shown to far outperform traditional polynomial or Gaussian RBF kernels. Moreover, we observed that a simple remapping of the input $x_i rightarrow x_i^a$ improves the performance of linear SVMs to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.

GZIP [BibTex]

GZIP [BibTex]


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Classifying LEP data with support vector algorithms.

Vannerem, P., Müller, K., Smola, A., Schölkopf, B., Söldner-Rembold, S.

In Artificial Intelligence in High Energy Nuclear Physics 99, Artificial Intelligence in High Energy Nuclear Physics 99, 1999 (inproceedings)

[BibTex]

[BibTex]


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Generalization Bounds via Eigenvalues of the Gram matrix

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

(99-035), NeuroCOLT, 1999 (techreport)

[BibTex]

[BibTex]


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Pedestal effects with periodic pulse trains

Henning, G., Wichmann, F.

Perception, 28, pages: S137, 1999 (poster)

Abstract
It is important to know for theoretical reasons how performance varies with stimulus contrast. But, for objects on CRT displays, retinal contrast is limited by the linear range of the display and the modulation transfer function of the eye. For example, with an 8 c/deg sinusoidal grating at 90% contrast, the contrast of the retinal image is barely 45%; more retinal contrast is required, however, to discriminate among theories of contrast discrimination (Wichmann, Henning and Ploghaus, 1998). The stimulus with the greatest contrast at any spatial-frequency component is a periodic pulse train which has 200% contrast at every harmonic. Such a waveform cannot, of course, be produced; the best we can do with our Mitsubishi display provides a contrast of 150% at an 8-c/deg fundamental thus producing a retinal image with about 75% contrast. The penalty of using this stimulus is that the 2nd harmonic of the retinal image also has high contrast (with an emmetropic eye, more than 60% of the contrast of the 8-c/deg fundamental ) and the mean luminance is not large (24.5 cd/m2 on our display). We have used standard 2-AFC experiments to measure the detectability of an 8-c/deg pulse train against the background of an identical pulse train of different contrasts. An unusually large improvement in detetectability was measured, the pedestal effect or "dipper," and the dipper was unusually broad. The implications of these results will be discussed.

[BibTex]

[BibTex]


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Apprentissage Automatique et Simplicite

Bousquet, O.

Biologische Kybernetik, 1999, In french (diplomathesis)

PostScript [BibTex]

PostScript [BibTex]


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p73 and p63 are homotetramers capable of weak heterotypic interactions with each other but not with p53.

Davison, T., Vagner, C., Kaghad, M., Ayed, A., Caput, D., CH, ..

Journal of Biological Chemistry, 274(26):18709-18714, June 1999 (article)

Abstract
Mutations in the p53 tumor suppressor gene are the most frequent genetic alterations found in human cancers. Recent identification of two human homologues of p53 has raised the prospect of functional interactions between family members via a conserved oligomerization domain. Here we report in vitro and in vivo analysis of homo- and hetero-oligomerization of p53 and its homologues, p63 and p73. The oligomerization domains of p63 and p73 can independently fold into stable homotetramers, as previously observed for p53. However, the oligomerization domain of p53 does not associate with that of either p73 or p63, even when p53 is in 15-fold excess. On the other hand, the oligomerization domains of p63 and p73 are able to weakly associate with one another in vitro. In vivo co-transfection assays of the ability of p53 and its homologues to activate reporter genes showed that a DNA-binding mutant of p53 was not able to act in a dominant negative manner over wild-type p73 or p63 but that a p73 mutant could inhibit the activity of wild-type p63. These data suggest that mutant p53 in cancer cells will not interact with endogenous or exogenous p63 or p73 via their respective oligomerization domains. It also establishes that the multiple isoforms of p63 as well as those of p73 are capable of interacting via their common oligomerization domain.

Web [BibTex]

Web [BibTex]


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Classification on proximity data with LP-machines

Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K., Obermayer, K., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 304-309, Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel-dependent support vector error bounds

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

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 103-108 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Linear programs for automatic accuracy control in regression

Smola, A., Schölkopf, B., Rätsch, G.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 575-580 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Is the Hippocampus a Kalman Filter?

Bousquet, O., Balakrishnan, K., Honavar, V.

In Proceedings of the Pacific Symposium on Biocomputing, 3, pages: 619-630, Proceedings of the Pacific Symposium on Biocomputing, 1999 (inproceedings)

[BibTex]

[BibTex]


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A Comparison of Artificial Neural Networks and Cluster Analysis for Typing Biometrics Authentication

Maisuria, K., Ong, CS., Lai, .

In unknown, pages: 9999-9999, International Joint Conference on Neural Networks, 1999 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Shrinking the tube: a new support vector regression algorithm

Schölkopf, B., Bartlett, P., Smola, A., Williamson, R.

In Advances in Neural Information Processing Systems 11, pages: 330-336 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Machine Learning and Language Acquisition: A Model of Child’s Learning of Turkish Morphophonology

Altun, Y.

Middle East Technical University, Ankara, Turkey, 1999 (mastersthesis)

[BibTex]


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Regularized principal manifolds.

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

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 214-229 , Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Entropy numbers, operators and support vector kernels.

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

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 285-299, Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Sparse kernel feature analysis

Smola, A., Mangasarian, O., Schölkopf, B.

(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

PostScript [BibTex]

PostScript [BibTex]


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A., Lengauer, T., Müller, K.

In German Conference on Bioinformatics (GCB 1999), October 1999 (inproceedings)

Abstract
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro­ teins start, the so­called translation initiation sites (TIS). This can be modeled as a classification prob­ lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc­ cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.

Web [BibTex]

Web [BibTex]


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Semiparametric support vector and linear programming machines

Smola, A., Friess, T., Schölkopf, B.

In Advances in Neural Information Processing Systems 11, pages: 585-591 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, Twelfth Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.

PDF Web [BibTex]

PDF Web [BibTex]


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Implications of the pedestal effect for models of contrast-processing and gain-control

Wichmann, F., Henning, G.

OSA Conference Program, pages: 62, 1999 (poster)

Abstract
Understanding contrast processing is essential for understanding spatial vision. Pedestal contrast systematically affects slopes of functions relating 2-AFC contrast discrimination performance to pedestal contrast. The slopes provide crucial information because only full sets of data allow discrimination among contrast-processing and gain-control models. Issues surrounding Weber's law will also be discussed.

[BibTex]


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Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten

Schölkopf, B., Müller, K., Smola, A.

Informatik - Forschung und Entwicklung, 14(3):154-163, September 1999 (article)

Abstract
We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explaining the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM) and Kernel PCA (Principal Component Analysis). More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, including ones where we obtained benchmark record results.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Kernel PCA and De-noising in feature spaces

Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., Rätsch, G.

In Advances in Neural Information Processing Systems 11, pages: 536-542 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.

PDF Web [BibTex]

PDF Web [BibTex]


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Input space versus feature space in kernel-based methods

Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K., Rätsch, G., Smola, A.

IEEE Transactions On Neural Networks, 10(5):1000-1017, September 1999 (article)

Abstract
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data.

Web DOI [BibTex]

Web DOI [BibTex]


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Advances in Kernel Methods - Support Vector Learning

Schölkopf, B., Burges, C., Smola, A.

MIT Press, Cambridge, MA, 1999 (book)

[BibTex]

[BibTex]


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Fisher discriminant analysis with kernels

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.

In Proceedings of the 1999 IEEE Signal Processing Society Workshop, 9, pages: 41-48, (Editors: Y-H Hu and J Larsen and E Wilson and S Douglas), IEEE, Neural Networks for Signal Processing IX, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Entropy numbers, operators and support vector kernels.

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

In Advances in Kernel Methods - Support Vector Learning, pages: 127-144, (Editors: B Schölkopf and CJC Burges and AJ Smola), MIT Press, Cambridge, MA, 1999 (inbook)

[BibTex]

[BibTex]


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Unexpected and anticipated pain: identification of specific brain activations by correlation with reference functions derived form conditioning theory

Ploghaus, A., Clare, S., Wichmann, F., Tracey, I.

29, 29th Annual Meeting of the Society for Neuroscience (Neuroscience), October 1999 (poster)

[BibTex]

[BibTex]