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- D Peleg
- 1996

This paper provides an overview of recent developments concerning the process of local majority voting in graphs, and its basic properties, from graph theoretic and algorithmic standpoints.

Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are… (More)

- Y Ben-Asher, D Peleg, R Ramaswami, A Schuster, Eshcol Fellowship
- 1998

This paper concerns the computational aspects of the reconngurable network model. The computational power of the model is investigated under several network topologies and assuming several variants of the model. In particular, it is shown that there are reconngurable machines based on simple network topologies, that are capable of solving large classes of… (More)

We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the <i>"L</i><inf>0</inf> norm") as a regularizing term instead of the <i>L</i><inf>1</inf> or <i>L</i><inf>2</inf> norms. In order to solve the optimization problem we use the cross entropy method to search over the possible… (More)

Sparsity plays an important role in many fields of engineering. The cardinality penalty function, often used as a measure of sparsity, is neither continuous nor differentiable and therefore smooth optimization algorithms cannot be applied directly. In this paper we present a continuous yet non-differentiable sparsity function which constitutes a tight lower… (More)

A new sparsity driven kernel classifier is presented based on the minimization of a recently derived data-dependent generalization error bound. The objective function consists of the usual hinge loss function penalizing training errors and a concave penalty function of the expansion coefficients. The problem of minimizing the non-convex bound is addressed… (More)

- Y Ben-Asher, K.-J Lange, D Peleg, A Schuster
- 1992

This paper concerns some of the theoretical complexity aspects of the reconngurable network model. The computational power of the model is investigated under several variants, depending on the type of switches (or switch operations) assumed by the network nodes. Computational power is evaluated by focusing on the set of problems computable in constant time… (More)

- Alexander Brook, Ran El-Yaniv, Eran Isler, Ron Kimmel, Ron Meir, Dori Peleg
- 2008

A fully automatic method for breast cancer diagnosis based on microscopic biopsy images is presented. The method achieves high recognition rates by applying multi-class support vector machines on generic feature vectors that are based on level-set statistics of the images. We also consider the problem of classification with rejection and show preliminary… (More)

- Dori Peleg, Ron Meir
- NIPS
- 2004

A novel linear feature selection algorithm is presented based on the global minimization of a data-dependent generalization error bound. Feature selection and scaling algorithms often lead to non-convex optimization problems, which in many previous approaches were addressed through gradient descent procedures that can only guarantee convergence to a local… (More)

- Y Ben-Asher, D Peleg, A Schuster, bullet D Peleg
- 2015

This paper concerns some of the theoretical complexity aspects of the reconfigurable network model. The computational power of the model is investigated under several variants, depending on the type of switches (or switch operations) assumed by the network nodes. Computational power is evaluated by focusing on the set of problems computable in constant time… (More)

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