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2388 results (BibTeX)

2000


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Solving Satisfiability Problems with Genetic Algorithms

Harmeling, S.

In Genetic Algorithms and Genetic Programming at Stanford 2000, pages: 206-213, (Editors: Koza, J. R.), Stanford Bookstore, Stanford, CA, USA, June 2000 (inbook)

Abstract
We show how to solve hard 3-SAT problems using genetic algorithms. Furthermore, we explore other genetic operators that may be useful to tackle 3-SAT problems, and discuss their pros and cons.

PDF [BibTex]

2000

PDF [BibTex]


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Robust ensemble learning

Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.

In Advances in Large Margin Classifiers, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

[BibTex]


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Intelligence as a Complex System

Zhou, D.

Biologische Kybernetik, 2000 (phdthesis)

[BibTex]

[BibTex]


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Neural Networks in Robot Control

Peters, J.

Biologische Kybernetik, Fernuniversität Hagen, Hagen, Germany, 2000 (diplomathesis)

[BibTex]

[BibTex]


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Choosing nu in support vector regression with different noise models — theory and experiments

Chalimourda, A., Schölkopf, B., Smola, A.

In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, International Joint Conference on Neural Networks, 2000 (inproceedings)

[BibTex]

[BibTex]


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Bayesian modelling of fMRI time series

, PADFR., Rasmussen, CE., Hansen, LK.

In pages: 754-760, (Editors: Sara A. Solla, Todd K. Leen and Klaus-Robert Müller), 2000 (inproceedings)

Abstract
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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v-Arc: Ensemble Learning in the Presence of Outliers

Rätsch, G., Schölkopf, B., Smola, A., Müller, K., Onoda, T., Mika, S.

In Advances in Neural Information Processing Systems 12, pages: 561-567, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
AdaBoost and other ensemble methods have successfully been applied to a number of classification tasks, seemingly defying problems of overfitting. AdaBoost performs gradient descent in an error function with respect to the margin, asymptotically concentrating on the patterns which are hardest to learn. For very noisy problems, however, this can be disadvantageous. Indeed, theoretical analysis has shown that the margin distribution, as opposed to just the minimal margin, plays a crucial role in understanding this phenomenon. Loosely speaking, some outliers should be tolerated if this has the benefit of substantially increasing the margin on the remaining points. We propose a new boosting algorithm which allows for the possibility of a pre-specified fraction of points to lie in the margin area or even on the wrong side of the decision boundary.

PDF Web [BibTex]

PDF Web [BibTex]


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Observational Learning with Modular Networks

Shin, H., Lee, H., Cho, S.

In Lecture Notes in Computer Science (LNCS 1983), LNCS 1983, pages: 126-132, Springer-Verlag, Heidelberg, International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), July 2000 (inproceedings)

Abstract
Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different weighting factors now that are inversely proportional to the distance from the input vector to the cluster centers. Comparison with bagging and boosting shows that the proposed approach reduces generalization error with a smaller number of networks employed.

PDF [BibTex]

PDF [BibTex]


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An Introduction to Kernel-Based Learning Algorithms

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

In Handbook of Neural Network Signal Processing, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)

[BibTex]

[BibTex]


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Entropy numbers for convex combinations and MLPs

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

In Advances in Large Margin Classifiers, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

[BibTex]

[BibTex]


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A Meanfield Approach to the Thermodynamics of a Protein-Solvent System with Application to the Oligomerization of the Tumour Suppressor p53.

Noolandi, J., Davison, TS., Vokel, A., Nie, F., Kay, C., Arrowsmith, C.

Proceedings of the National Academy of Sciences of the United States of America, 97(18):9955-9960, August 2000 (article)

Web [BibTex]

Web [BibTex]


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A High Resolution and Accurate Pentium Based Timer

Ong, CS., Wong, F., Lai, WK.

In 2000 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Invariant feature extraction and classification in kernel spaces

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

In Advances in neural information processing systems 12, pages: 526-532, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Robust Ensemble Learning for Data Mining

Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.

In Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1805, pages: 341-341, Lecture Notes in Artificial Intelligence, (Editors: H. Terano), Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000 (inproceedings)

[BibTex]

[BibTex]


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Ensemble of Specialized Networks based on Input Space Partition

Shin, H., Lee, H., Cho, S.

In Proc. of the Korean Operations Research and Management Science Conference, pages: 33-36, Korean Operations Research and Management Science Conference, October 2000 (inproceedings)

[BibTex]

[BibTex]


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Sparse greedy matrix approximation for machine learning.

Smola, A., Schölkopf, B.

In 17th International Conference on Machine Learning, Stanford, 2000, pages: 911-918, (Editors: P Langley), Morgan Kaufman, San Fransisco, CA, USA, 17th International Conference on Machine Learning (ICML), 2000 (inproceedings)

[BibTex]

[BibTex]


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DES Approach Failure Recovery of Pump-valve System

Son, HI., Kim, KW., Lee, S.

In Korean Society of Precision Engineering (KSPE) Conference, pages: 647-650, Annual Meeting of the Korean Society of Precision Engineering (KSPE), October 2000 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Natural Regularization from Generative Models

Oliver, N., Schölkopf, B., Smola, A.

In Advances in Large Margin Classifiers, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

[BibTex]


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The Kernel Trick for Distances

Schölkopf, B.

(MSR-TR-2000-51), Microsoft Research, Redmond, WA, USA, 2000 (techreport)

Abstract
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as normbased distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.

PDF Web [BibTex]

PDF Web [BibTex]


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Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen

Staedtgen, M., Hahn, S., Franz, MO., Spitzer, M.

pages: 98, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot), 3. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2000 (poster)

Abstract
Durch moderne Bildverarbeitungstechnologien ist es m{\"o}glich, in Kraftfahrzeugen bestimmte kritische Verkehrssituationen automatisch zu erkennen und den Fahrer zu warnen bzw. zu informieren. Ein Problem ist dabei die Darbietung der Ergebnisse, die den Fahrer m{\"o}glichst wenig belasten und seine Aufmerksamkeit nicht durch zus{\"a}tzliche Warnleuchten oder akustische Signale vom Verkehrsgeschehen ablenken soll. In einer Reihe von Experimenten wurde deshalb untersucht, ob subliminal dargebotene, das heißt nicht bewußt wahrgenommene, verkehrsrelevante Informationen verhaltenswirksam werden und zur Informations{\"u}bermittlung an den Fahrer genutzt werden k{\"o}nnen. In einem Experiment zur semantischen Bahnung konnte mit Hilfe einer lexikalischen Entscheidungsaufgabe gezeigt werden, daß auf den Straßenverkehr bezogene Worte schneller verarbeitet werden, wenn vorher ein damit in Zusammenhang stehendes Bild eines Verkehrsschildes subliminal pr{\"a}sentiert wurde. Auch bei parafovealer Darbietung der subliminalen Stimuli wurde eine Beschleunigung erzielt. In einer visuellen Suchaufgabe wurden in Bildern realer Verkehrssituationen Verkehrszeichen schneller entdeckt, wenn das Bild des Verkehrszeichens vorher subliminal dargeboten wurde. In beiden Experimenten betrug die Pr{\"a}sentationszeit f{\"u}r die Hinweisreize 17 ms, zus{\"a}tzlich wurde durch Vorw{\"a}rts- und R{\"u}ckw{\"a}rtsmaskierung die bewußteWahrnehmung verhindert. Diese Laboruntersuchungen zeigten, daß sich auch im Kontext des Straßenverkehrs Beschleunigungen der Informationsverarbeitung durch subliminal dargebotene Stimuli erreichen lassen. In einem dritten Experiment wurde die Darbietung eines subliminalen Hinweisreizes auf die Reaktionszeit beim Bremsen in einem realen Fahrversuch untersucht. Die Versuchspersonen (n=17) sollten so schnell wie m{\"o}glich bremsen, wenn die Bremsleuchten eines im Abstand von 12-15 m voran fahrenden Fahrzeuges aufleuchteten. In 50 von insgesamt 100 Durchg{\"a}ngen wurde ein subliminaler Stimulus (zwei rote Punkte mit einem Zentimeter Durchmesser und zehn Zentimeter Abstand) 150 ms vor Aufleuchten der Bremslichter pr{\"a}sentiert. Die Darbietung erfolgte durch ein im Auto an Stelle des Tachometers integriertes TFT-LCD Display. Im Vergleich zur Reaktion ohne subliminalen Stimulus verk{\"u}rzte sich die Reaktionszeit dadurch signifikant um 51 ms. In den beschriebenen Experimenten konnte gezeigt werden, daß die subliminale Darbietung verkehrsrelevanter Information auch in Kraftfahrzeugen verhaltenswirksam werden kann. In Zukunft k{\"o}nnte durch die Kombination der online-Bildverarbeitung im Kraftfahrzeug mit subliminaler Darbietung der Ergebnisse eine Erh{\"o}hung der Verkehrssicherheit und des Komforts erreicht werden.

Web [BibTex]

Web [BibTex]


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Three-dimensional reconstruction of planar scenes

Urbanek, M.

Biologische Kybernetik, INP Grenoble, Warsaw University of Technology, September 2000 (diplomathesis)

Abstract
For a planar scene, we propose an algorithm to estimate its 3D structure. Homographies between corresponding planes are employed in order to recover camera motion parameters - between camera positions from which images of the scene were taken. Cases of one- and multiple- corresponding planes present on the scene are distinguished. Solutions are proposed for both cases.

ZIP [BibTex]

ZIP [BibTex]


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Transductive Inference for Estimating Values of Functions

Chapelle, O., Vapnik, V., Weston, J.

In Advances in Neural Information Processing Systems 12, pages: 421-427, (Editors: Solla, S.A. , T.K. Leen, K-R Müller), MIT Press, Cambridge, MA, USA, Thirteenth Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
We introduce an algorithm for estimating the values of a function at a set of test points $x_1^*,dots,x^*_m$ given a set of training points $(x_1,y_1),dots,(x_ell,y_ell)$ without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) is more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.

PDF Web [BibTex]

PDF Web [BibTex]


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Advances in Large Margin Classifiers

Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D.

pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

Abstract
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

Web [BibTex]

Web [BibTex]


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Model Selection for Support Vector Machines

Chapelle, O., Vapnik, V.

In Advances in Neural Information Processing Systems 12, pages: 230-236, (Editors: Solla, S.A. , T.K. Leen, K-R Müller), MIT Press, Cambridge, MA, USA, Thirteenth Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that using these functionals, one can both predict the best choice of parameters of the model and the relative quality of performance for any value of parameter.

PDF Web [BibTex]

PDF Web [BibTex]


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A Simple Iterative Approach to Parameter Optimization

Zien, A., Zimmer, R., Lengauer, T.

Journal of Computational Biology, 7(3,4):483-501, November 2000 (article)

Abstract
Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a scoring function combines the values for different parameters of possible sequence-to-structure alignments into a single score to allow for unambiguous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, a partial ordering on optimal alignments to other structures, e.g., derived from structural comparisons, may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a heuristic approach: iterating the computation of solutions (here, threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via systematic calibration methods. For our application (i.e., threading), this iterative approach results in structurally meaningful weights that significantly improve performance on both the training and the test data sets. In addition, the optimized parameters show significant improvements on the recognition rate for a grossly enlarged comprehensive benchmark, a modified recognition protocol as well as modified alignment types (local instead of global and profiles instead of single sequences). These results show the general validity of the optimized weights for the given threading program and the associated scoring contributions.

Web [BibTex]

Web [BibTex]


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A real-time model of the human knee for application in virtual orthopaedic trainer

Peters, J., Riener, R.

In Proceedings of the 10th International Conference on BioMedical Engineering (ICBME 2000), 10, pages: 1-2, 10th International Conference on BioMedical Engineering (ICBME) , December 2000 (inproceedings)

Abstract
In this paper a real-time capable computational model of the human knee is presented. The model describes the passive elastic joint characteristics in six degrees-of-freedom (DOF). A black-box approach was chosen, where experimental data were approximated by piecewise polynomial functions. The knee model has been applied in a the Virtual Orthopaedic Trainer, which can support training of physical knee evaluation required for diagnosis and surgical planning.

PDF Web [BibTex]

PDF Web [BibTex]


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Ensemble Learning Algorithm of Specialized Networks

Shin, H., Lee, H., Cho, S.

In Proc. of the Korea Information Science Conference, pages: 308-310, Korea Information Science Conference, October 2000 (inproceedings)

[BibTex]

[BibTex]


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The entropy regularization information criterion

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

In Advances in Neural Information Processing Systems 12, pages: 342-348, (Editors: SA Solla and TK Leen and K-R Müller), MIT Press, Cambridge, MA, USA, 13th Annual Neural Information Processing Systems Conference (NIPS), June 2000 (inproceedings)

Abstract
Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector machines, where good bounds are obtainable by the entropy number approach. We extend these methods to systems with expansions in terms of arbitrary (parametrized) basis functions and a wide range of regularization methods covering the whole range of general linear additive models. This is achieved by a data dependent analysis of the eigenvalues of the corresponding design matrix.

PDF Web [BibTex]

PDF Web [BibTex]


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DES Approach Failure Diagnosis of Pump-valve System

Son, HI., Kim, KW., Lee, S.

In Korean Society of Precision Engineering (KSPE) Conference, pages: 643-646, Annual Meeting of the Korean Society of Precision Engineering (KSPE), October 2000 (inproceedings)

Abstract
As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

PDF [BibTex]

PDF [BibTex]


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Entropy Numbers of Linear Function Classes.

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

In 13th Annual Conference on Computational Learning Theory, pages: 309-319, (Editors: N Cesa-Bianchi and S Goldman), Morgan Kaufman, San Fransisco, CA, USA, 13th Annual Conference on Computational Learning Theory (COLT), 2000 (inproceedings)

[BibTex]

[BibTex]


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Kernel method for percentile feature extraction

Schölkopf, B., Platt, J., Smola, A.

(MSR-TR-2000-22), Microsoft Research, 2000 (techreport)

Abstract
A method is proposed which computes a direction in a dataset such that a speci􏰘ed fraction of a particular class of all examples is separated from the overall mean by a maximal margin􏰤 The pro jector onto that direction can be used for class􏰣speci􏰘c feature extraction􏰤 The algorithm is carried out in a feature space associated with a support vector kernel function􏰢 hence it can be used to construct a large class of nonlinear fea􏰣 ture extractors􏰤 In the particular case where there exists only one class􏰢 the method can be thought of as a robust form of principal component analysis􏰢 where instead of variance we maximize percentile thresholds􏰤 Fi􏰣 nally􏰢 we generalize it to also include the possibility of specifying negative examples􏰤

PDF [BibTex]

PDF [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]