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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

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.

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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

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. © NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.

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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Extracting Relevant Structures with Side Information 02

Extracting Relevant Structures with Side Information

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Annual Conference on Neural Information Processing Systems

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

ISBN: (纸本)0262025507

The problem of extracting the relevant aspects of data, in face of multiple conflicting structures, is inherent to modeling of complex data. Extracting structure in one random variable that is relevant for another variable has been principally addressed recently via the information bottleneck method. However, such auxiliary variables often contain more information man is actually required due to structures that are irrelevant for the task. In many other cases it is in fact easier to specify what is irrelevant than what is, for the task at hand. Identifying the relevant structures, however, can thus be considerably improved by also minimizing the information about another, irrelevant, variable. In this paper we give a general formulation of this problem and derive its formal, as well as algorithmic, solution. Its operation is demonstrated in a synthetic example and in two real world problems in the context of text categorization and face images. While the original information bottleneck problem is related to rate distortion theory, with the distortion measure replaced by the relevant information, extracting relevant features while removing Irrelevant ones is related to rate distortion with side information.

关键词： Distortion measurement information side information Rate distortion theory Text categorization World problems Rate distortion Random variable Variable Traffic Bottlenecks

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Cross-dataset Clustering: Revealing Corresponding Themes Across Multiple Corpora 6

Cross-dataset Clustering: Revealing Corresponding Themes Acr...

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6th Conference on Natural Language Learning, CoNLL 2002

作者： Dagan, Ido Marx, Zvika Shamir, Eli Department of Computer Science Bar-Ilan University Ramat-Gan52900 Israel LingoMotors Inc United States Center for Neural Computation The Hebrew University CS Dept. Bar-Ilan University Ramat-Gan52900 Israel School of Computer Science and Engineering The Hebrew University Jerusalem91904 Israel

We present a method for identifying corresponding themes across several corpora that are focused on related, but distinct, domains. This task is approached through simultaneous clustering of keyword sets extracted from the analyzed corpora. Our algorithm extends the information-bottleneck soft clustering method for a suitable setting consisting of several datasets. Experimentation with topical corpora reveals similar aspects of three distinct religions. The evaluation is by way of comparison to clusters constructed manually by an expert. © 2002 Proceedings of the Annual Meeting of the Association for computational Linguistics. All Rights Reserved.

关键词： Cluster analysis

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Universality and individuality in a neural code

Universality and individuality in a neural code

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14th Annual Neural Information Processing Systems Conference, NIPS 2000

作者： Schneidman, Elad Brenner, Naama Tishby, Naftali De Ruyter Van Steveninck, Rob R. Bialek, William School of Computer Science and Engineering Center for Neural Computation Jerusalem 91904 Israel Department of Neurobiology Hebrew University Jerusalem 91904 Israel NEC Research Institute 4 Independence Way Princeton NJ 08540 United States

ISBN: (纸本)0262122413

The problem of neural coding is to understand how sequences of action potentials (spikes) are related to sensory stimuli, motor outputs, or (ultimately) thoughts and intentions. One clear question is whether the same coding rules are used by different neurons, or by corresponding neurons in different individuals. We present a quantitative formulation of this problem using ideas from information theory, and apply this approach to the analysis of experiments in the fly visual system. We find significant individual differences in the structure of the code, particularly in the way that temporal patterns of spikes are used to convey information beyond that available from variations in spike rate. On the other hand, all the flies in our ensemble exhibit a high coding efficiency, so that every spike carries the same amount of information in all the individuals. Thus the neural code has a quantifiable mixture of individuality and universality.

关键词： Electrophysiology

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Group redundancy measures reveal redundancy reduction in the auditory pathway 01

Group redundancy measures reveal redundancy reduction in the...

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Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic

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

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 the acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and AI neurons. These results provide for the first time a direct evidence for redundancy reduction along the ascending auditory pathway, as has been hypothesized for theoretical considerations [Barlow 1959,2001]. The redundancy effects 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 that contain redundancies due to the statistical structure of natural stimuli, into a representation in which cortical neurons extract rare and independent component of complex acoustic signals, that are useful for auditory scene analysis.

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Data clustering by Markovian relaxation and the Information Bottleneck Method 00

Data clustering by Markovian relaxation and the Information ...

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Annual Conference on Neural Information Processing Systems

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

ISBN: (纸本)0262122413

We introduce a new, non-parametric and principled, distance based clustering method. This method combines a pairwise based approach with a vector-quantization method which provide a meaningful interpretation to the resulting clusters. The idea is based on turning the distance matrix into a Markov process and then examine the decay of mutual-information during the relaxation of this process. The clusters emerge as quasi-stable structures during this relaxation, and then are extracted using the information bottleneck method. These clusters capture the information about the initial point of the relaxation in the most effective way. The method can cluster data with no geometric or other bias and makes no assumption about the underlying distribution.

关键词： Markov chain zero point distance matrix Traffic Bottlenecks data clustering information quasi-steady states

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