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16th Annual neural Information Processing Systems Conference, NIPS 2002

作者： Shpigelman, Lavi Singer, Yoram Paz, Rony Vaadia, Eilon School of Computer Science and Engineering Hebrew University Jerusalem 91904 Israel Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel Dept. of Physiology Hadassah Medical School Hebrew University Jerusalem 91904 Israel

ISBN: (纸本)0262025507

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologically-motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach using the Spikernel and various standard kernels for the task of predicting hand movement velocities from cortical recordings. In all of our experiments all the kernels we tested outperform the standard scalar product used in regression with the Spikernel consistently achieving the best performance.

关键词： Vector spaces

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Enhancing image and video retrieval: learning via equivalence constraints

Enhancing image and video retrieval: learning via equivalenc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： T. Hertz N. Shental A. Bar-Hillel D. Weinshall School of Computer Science and Engineering and the Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

The paper is about learning using partial information in the form of equivalence constraints. Equivalence constraints provide relational information about the labels of data points, rather than the labels themselves. Our work is motivated by the observation that in many real life applications partial information about the data can be obtained with very little cost. For example, in video indexing we may want to use the fact that a sequence of faces obtained from successive frames in roughly the same location is likely to contain the same unknown individual. Learning using equivalence constraints is different from learning using labels and poses new technical challenges. In this paper we present three novel methods for clustering and classification, which use equivalence constraints. We provide results of our methods on a distributed image querying system that works on a large facial image database, and on the clustering and retrieval of surveillance data. Our results show that we can significantly improve the performance of image retrieval by taking advantage of such assumptions as temporal continuity in the data. Significant improvement is also obtained by making the users of the system take the role of distributed teachers, which reduces the need for expensive labeling by paid human labor.

关键词： Image retrieval Information retrieval Indexing Surveillance Humans Training data Data mining computer science Costs Image databases

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Information bottleneck for Gaussian variables 03

Information bottleneck for Gaussian variables

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Proceedings of the 17th International Conference on neural Information Processing Systems

作者： Gal Chechik Amir Globerson Naftali Tishby Yair Weiss School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem Israel

The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another - relevance - variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations that preserve relevant information, rather than discrete clusters. We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation for the important case of multivariate Gaussian variables. The obtained optimal representation is a noisy linear projection to eigenvectors of the normalized correlation matrix ∑x|y∑−1x, which is also the basis obtained in Canonical Correlation Analysis. However, in Gaussian IB, the compression tradeoff parameter uniquely determines the dimension, as well as the scale of each eigenvector. This introduces a novel interpretation where solutions of different ranks lie on a continuum parametrized by the compression level. Our analysis also provides an analytic expression for the optimal tradeoff - the information curve - in terms of the eigenvalue spectrum.

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Distributional word clusters vs. words for text categorization

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Journal of Machine Learning Research 2003年 3卷 1183-1208页

作者： Bekkerman, Ron El-Yaniv, Ran Tishby, Naftali Winter, Yoad Department of Computer Science Technion - Israel Institute of Technology Haifa 32000 Israel School of Computer Science and Engineering Center for Neural Computation Hebrew University Jerusalem 91904 Israel

We study an approach to text categorization that combines distributional clustering of words and a Support Vector Machine (SVM) classifier. This word-cluster representation is computed using the recently introduced Information Bottleneck method, which generates a compact and efficient representation of documents. When combined with the classification power of the SVM, this method yields high performance in text categorization. This novel combination of SVM with word-cluster representation is compared with SVM-based categorization using the simpler bag-of-words (BOW) representation. The comparison is performed over three known datasets. On one of these datasets (the 20 Newsgroups) the method based on word clusters significantly outperforms the word-based representation in terms of categorization accuracy or representation efficiency. On the two other sets (Reuters-21578 and WebKB) the word-based representation slightly outperforms the word-cluster representation. We investigate the potential reasons for this behavior and relate it to structural differences between the datasets. © 2003 Ron Bekkerman, Ran El-Yaniv, Naftali Tishby, and Yoad Winter.

关键词： Support vector machines

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Sufficient dimensionality reduction

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2003年 3卷

作者： Amir Globerson Naftali Tishby School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem 91904 Israel

Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. In contrast with previously introduced clustering based approaches, here we extract continuous feature functions directly from the co-occurrence matrix. In a sense, we automatically extract functions of the variables that serve as approximate sufficient statistics for a sample of one variable about the other one. Our method is different from dimensionality reduction methods which are based on a specific, sometimes arbitrary, metric or embedding. Another interpretation of our method is as generalized - multi-dimensional - non-linear regression, where rather than fitting one regression function through two dimensional data, we extract d -regression functions whose expectation values capture the information among the variables. It thus presents a new learning paradigm that unifies aspects from both supervised and unsupervised learning. The resulting dimension reduction can be described by two conjugate d-dimensional differential manifolds that are coupled through Maximum Entropy I -projections. The Riemannian metrics of these manifolds are determined by the observed expectation values of our extracted features. Following this geometric interpretation we present an iterative information projection algorithm for finding such features and prove its convergence. Our algorithm is similar to the method of "association analysis" in statistics, though the feature extraction context as well as the information theoretic and geometric interpretation are new. The algorithm is illustrated by various synth

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Distributional word clusters vs. words for text categorization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2003年 3卷

作者： Ron Bekkerman Ran El-Yaniv Naftali Tishby Yoad Winter Department of Computer Science Technion - Israel Institute of Technology Haifa 32000 Israel School of Computer Science and Engineering and Center for Neural Computation The Hebrew University Jerusalem 91904 Israel

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Coupled clustering: a method for detecting structural correspondence

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2003年 3卷

作者： Zvika Marx Ido Dagan Joachim M. Buhmann Eli Shamir The Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem Givat-Ram Jerusalem 91904 Israel and Department of Computer Science Bar-Ilan University Ramat-Gan 52900 Israel Department of Computer Science Bar-Ilan University Ramat-Gan 52900 Israel Institut für Informatik III University of Bonn Römerstr. 164 D-53117 Bonn Germany School of Computer Science and Engineering The Hebrew University of Jerusalem Givat-Ram Jerusalem 91904 Israel

This paper proposes a new paradigm and a computational framework for revealing equivalencies (analogies) between sub-structures of distinct composite systems that are initially represented by unstructured data sets. For this purpose, we introduce and investigate a variant of traditional data clustering, termed coupled clustering, which outputs a configuration of corresponding subsets of two such representative sets. We apply our method to synthetic as well as textual data. Its achievements in detecting topical correspondences between textual corpora are evaluated through comparison to performance of human experts.

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Group redundancy measures reveal redundancy reduction in the auditory pathway

Group redundancy measures reveal redundancy reduction in the...

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15th Annual neural Information Processing Systems Conference, NIPS 2001

作者： Chechik, Gal Globerson, Amir Tishby, Naftali Anderson, Michael J. Young, Eric D. Nelken, Tsrael School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Israel Department of Biomedical Engineering Johns Hopkins University Baltimore MD United States Department of Physiology Hadassah Medical School Hebrew University of Jerusalem Israel

ISBN: (纸本)0262042088

The way groups of auditory neurons interact, to code acoustic information is investigated using an information theoretic approach. We develop measures of redundancy among groups of neurons, and apply them to the study of collaborative coding efficiency in two processing stations in the auditory pathway: The inferior colliculus (IC) and the primary auditory cortex (AI). Under two schemes for the coding of t he acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and Al neurons. These results provide for t he first time a direct evidence for redundancy reduction along the ascending auditory pat hway, as has been hypothesized for theoretical considerations [Barlow 1959.2001]. The redundancy effect s under the single-spikes coding scheme are significant only for groups larger than ten cells, and cannot be revealed with the redundancy measures that use only pairs of cells. The results suggest that the auditory system transforms low level representations t hat contain redundancies due t o t he statistical st ructure of natural stimuli, into a representation in which cortical neurons extract rare and independent component of complex acoustic signals, t hat are useful for auditory scene analysis.

关键词： Redundancy

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Spikernels: Embedding Spiking Neurons in Inner-Product Spaces 15

Spikernels: Embedding Spiking Neurons in Inner-Product Space...

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15th International Conference on neural Information Processing Systems, NIPS 2002

作者： Shpigelman, Lavi Singer, Yoram Paz, Rony Vaadia, Eilon School of Computer Science and Engineering The Hebrew University Jerusalem91904 Israel Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem91904 Israel Dept. of Physiology Hadassah Medical School The Hebrew University Jerusalem91904 Israel

ISBN: (纸本)0262025507

关键词： Vector spaces

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Margin Analysis of the LVQ Algorithm 02

Margin Analysis of the LVQ Algorithm

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Annual Conference on neural Information Processing Systems

作者： Koby Crammer Ran Gilad-Bachrach Amir Navot Naftali Tishby School of Computer Science and Engineering and Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel

ISBN: (纸本)0262025507

Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that the 20 years old Learning Vector Quantization (LVQ) algorithm emerges naturally from our framework.

关键词： algorithms PROTOTYPE Classifiers Tumor margin status complexity classes Generalization

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