Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Modern machine learning algorithms can extract useful information from text, images and videos. All these applications involve solving NP-hard problems in average case using heuris-tics. What properties of the input allow it to be solved efficiently? Theoretically analyzing the heuristics is often

Abstract. In this article, we describe the algorithm and study the performance of a Hessian free optimization technique applied to machine learning problems. We implement the commonly used black box model for optimization and solve a particular challenging recursive neural network learning problem

This paper defines a new machine learning problem to which standard machine learning algorithms cannot easily be applied. The problem occurs in the domain of lexical acquisition. The ambiguous and synonymous nature of words causes the difficulty of using standard induction techniques to learn a

We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points

. Plus, it has been shown by [10] that logic synthesis methods (specifically functional decomposition) can be used for finding solutions in the machine learning paradigm. One problem in machine learning is the concept of missing attributes (input don't know). The concept of a missing attribute

Abstract Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss

to reduce the search space of automatic provers. This paper presents an introduction to axiom selection as a machine learning problem and describes the challenges that distinguish it from other applications of machine learning. 1

of statistics and machine learning, either for analysis of data sets, or as a subgoal of a more complex problem. Traditionally parametric 1 models have been used for this purpose. These have a possible advantage in ease of interpretability, but for complex data sets, simple parametric models may lack expressive

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging