In this paper we develop a novel probabilistic generalization bound for regularized kernel learning algorithms. First, we show that generalization analysis of kernel learning algorithms reduces to investigation of the suprema of homogeneous Rademacher chaos process of order two over candidate kernels, which we refer to it as Rademacher chaos complexity. Our new methodology is based on the princ...

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Isin...

Berkeley’s approach to the Temporalia TIR retrieval task for NTCIR-11 has been, as is our custom with new tasks, to use our probabilistic text retrieval methods to establish an in-house baseline for future experiments. For our initial experiments we used only the Logistic Regression ranking both with and without pseudo relevance feedback. We have previously used these algorithms in the NTCIR-8 ...

A new heuristic solution procedure for the solution of the rectilinear Steiner problem is proposed. The procedure is based on an innovative neighborhood structure that is explored using a pseudo-greedy approach. The probabilistic rules designed to induce search diversification are based on move attributes derived from the neighborhood structure. A Taguchi design of experiments is used to find t...

Spread spectrum techniques are often employed when transmitting information. They are widely used in wireless and cellular telephony. Spread spectrum techniques allow one to partition bandwidth, to hide transmissions, and to protect one’s transmissions from being jammed. In this article, we develop two facets of spread spectrum. We show how to understand the properties of direct sequence spread...

In this paper we develop a novel probabilistic generalization bound for learning the kernel problem. First, we show that the generalization analysis of the kernel learning algorithms reduces to investigation of the suprema of the Rademacher chaos process of order two over candidate kernels, which we refer to as Rademacher chaos complexity. Next, we show how to estimate the empirical Rademacher ...

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The ...

Language model information retrieval depends on accurate estimation of document models. In this paper, we propose a document expansion technique to deal with the problem of insufficient sampling of documents. We construct a probabilistic neighborhood for each document, and expand the document with its neighborhood information. The expanded document provides a more accurate estimation of the doc...

Our work presents a new approach to developing, analyzing, and proving correctness of programs intended for execution on a quantum computer. We provide tools to write quantum as well as classical specifications, develop quantum and classical solutions for them, and analyses various properties of quantum specifications and quantum programs, such as implementability, time and space complexity, an...

The probabilistic escape LIFO (peLIFO) policy was proposed as a member of the pseudo-LIFO family of replacement policies [1]. This is a deterministic replacement policy that computes certain probabilities to reach a deterministic eviction decision in last-level caches. This brief note includes a few additional experimental results involving the peLIFO policy, a discussion on the impact of L2 ca...