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- Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
- Journal of Machine Learning Research
- 2001

We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We… (More)

- Zach Solan, David Horn, Eytan Ruppin, Shimon Edelman
- Proceedings of the National Academy of Sciences…
- 2005

We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm… (More)

- David Horn, Assaf Gottlieb
- Physical review letters
- 2002

We propose a novel clustering method that is based on physical intuition derived from quantum mechanics. Starting with given data points, we construct a scale-space probability function. Viewing the latter as the lowest eigenstate of a Schrödinger equation, we use simple analytic operations to derive a potential function whose minima determine cluster… (More)

- David L. Donoho, Xiaoming Huo, +13 authors S. Semmes
- 2001

We describe a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis. The framework has five key components. The beamlet dictionary is a dyadicallyorganized collection of line segments, occupying a range of dyadic locations and scales, and occurring at a range of orientations.… (More)

- Roy Varshavsky, Assaf Gottlieb, Michal Linial, David Horn
- ISMB
- 2006

MOTIVATION
Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy,… (More)

Based on an observation about the different effect of ensemble averaging on the bias and variance portions of the prediction error, we discuss training methodologies for ensembles of networks. We demonstrate the effect of variance reduction and present a method of extrapolation to the limit of an infinite ensemble. A significant reduction of variance is… (More)

- Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
- ICPR
- 2000

- Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman
- NIPS
- 2002

The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic structures from corpus data. We extend this principle by applying it recursively, and by using mutual information for estimating category coherence. The resulting… (More)

- Itai Sharon, Shani Tzahor, +13 authors Oded Béjà
- The ISME journal
- 2007

Cyanobacteria of the genera Synechococcus and Prochlorococcus are important contributors to photosynthetic productivity in the open ocean. The discovery of genes (psbA, psbD) that encode key photosystem II proteins (D1, D2) in the genomes of phages that infect these cyanobacteria suggests new paradigms for the regulation, function and evolution of… (More)

- Benny Chor, David Horn, Nick Goldman, Yaron Levy, Tim Massingham
- Genome Biology
- 2009

The empirical frequencies of DNA k-mers in whole genome sequences provide an interesting perspective on genomic complexity, and the availability of large segments of genomic sequence from many organisms means that analysis of k-mers with non-trivial lengths is now possible. We have studied the k-mer spectra of more than 100 species from Archea, Bacteria,… (More)