A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

A Note on Support Vector Machines Degeneracy

When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM training problems with dual optimal solutions with all coefficients at bounds, but that all such problems are degenerate in the sense that the "optimal separating hyperplane" is given by ${f w} = {f 0}$, and the resulting (degenerate) SVM will classify all future points identically (to the class that supplies more training data). We also derive necessary and sufficient conditions on the input data for this to occur. Finally, we show that an SVM training problem can always be made degenerate by the addition of a single data point belonging to a certain unboundedspolyhedron, which we characterize in terms of its extreme points and rays.

Lower Bounds for Achieving Synchronous Early Stopping Consensus with Orderly Crash Failures

In this paper, we discuss the consensus problem for synchronous distributed systems with orderly crash failures. For a synchronous distributed system of n processes with up to t crash failures and f failures actually occur, first, we present a bivalency argument proof to solve the open problem of proving the lower bound, min (t + 1, f + 2) rounds, for early-stopping synchronous consensus with orderly crash failures, where t < n - 1. Then, we extend the system model with orderly crash failures to a new model in which a process is allowed to send multiple messages to the same destination process in a round and the failing processes still respect the order specified by the protocol in sending messages. For this new model, we present a uniform consensus protocol, in which all non-faulty processes always decide and stop immediately by the end of f + 1 rounds. We prove that the lower bound of early stopping protocols for both consensus and uniform consensus are f + 1 rounds under the new model, and our proposed protocol is optimal.

Temporary and Permanent Buyout Prices in Online Auctions

Increasingly used in online auctions, buyout prices allow bidders to instantly purchase the item listed. We distinguish two types: a temporary buyout option disappears if a bid above the reserve price is made; a permanent one remains throughout the auction or until it is exercised. In a model featuring time-sensitive bidders with uniform valuations and Poisson arrivals but endogenous bidding times, we focus on finding temporary and permanent buyout prices maximizing the seller's discounted revenue, and examine the relative benefit of using each type of option in various environments. We characterize equilibrium bidder strategies in both cases and then solve the problem of maximizing seller's utility by simulation. Our numerical experiments suggest that buyout options may significantly increase a seller’s revenue. Additionally, while a temporary buyout option promotes early bidding, a permanent option gives an incentive to the bidders to bid late, thus leading to concentrated bids near the end of the auction.

Modeling and Analysis of Two-Part Type Manufacturing Systems

This paper presents a model and analysis of a synchronous tandem flow line that produces different part types on unreliable machines. The machines operate according to a static priority rule, operating on the highest priority part whenever possible, and operating on lower priority parts only when unable to produce those with higher priorities. We develop a new decomposition method to analyze the behavior of the manufacturing system by decomposing the long production line into small analytically tractable components. As a first step in modeling a production line with more than one part type, we restrict ourselves to the case where there are two part types. Detailed modeling and derivations are presented with a small two-part-type production line that consists of two processing machines and two demand machines. Then, a generalized longer flow line is analyzed. Furthermore, estimates for performance measures, such as average buffer levels and production rates, are presented and compared to extensive discrete event simulation. The quantitative behavior of the two-part type processing line under different demand scenarios is also provided.

Vint-i-cinc anys de docència, cinc lleis d'educació i una permanent sensació d'incertesa : una reflexió sobre l'educació amb la LOE com a teló de fons. 'Veinticinco años de docencia, cinco leyes de educación y una permanente sensación de incertidumbre : una reflexión sobre la educación con la LOE como telón de fondo'.

Describe la incertidumbre en que el trabajo de los docentes se ve inmerso, debido a los continuos cambios de gobierno, los cuales afectan al quehacer educativo a través de la creación de leyes. En medio del debate permanente los docentes se sienten huérfanos, solos, ante unas necesidades en las aulas que son difíciles de dar respuesta debido a la falta de formación, de habilidades y sobretodo de apoyo externo, de la sociedad y de la família.

Un model de formaci?? permanent de l'entrenador de futbol

Resumen tomado del autor

Percepci?? dels entrenadors espanyols de tennis d'alta competici?? sobre la seva formaci?? permanent : situaci?? actual i perspectives de futur

Resumen tomado del autor. Contiene tablas varias: de resultado, encuesta y gu??a de entrevista

Inspecció educativa i formació permanent del professorat. 'Inspección educativa y formación permanente del profesorado'.

Resumen tomado del autor

La formació a distància en el programa de formació permanent del professorat a les Illes Balears. 'La formación a distancia en el programa de formación permanente del profesorado en las Islas Baleares'.

Resumen tomado del autor

Programa de formació de formadors : cap a la professionalització dels assessors de formació permanent del professorat no universitari de les Illes Balears. 'Programa de formación de formadores : hacia la profesionalización de los asesores de formación permanente del profesorado no universitario de las Islas Baleares'.

Resumen tomado del autor

L'educació emocional en la formació permanent del professorat. 'La educación emocional en la formación permanente del profesorado'.

Resumen tomado de la revista

Ser mestre a l'escola petita : reflexions sobre la formaci?? inicial i permanent dels mestres. 'Ser maestro en la escuela peque??a : reflexiones sobre la formaci??n inicial y permanente de los maestros'

Se estudia la delicada situaci??n de las escuelas rurales y la gran importancia de la actuaci??n de los docentes que trabajan en ellas. Se analiza el perfil que tendr??a que tener el profesional que trabaja en estos centros y para ello se indica c??mo deber??an ser la formaci??n inicial y permanente espec??ficas del maestro rural, temas olvidados en los actuales planes de estudio de magisterio.