Classification trees are one of the most popular types of classifiers, with ease of implementation and interpretation being among their attractive features. Despite the widespread use of classification trees, theoretical analysis of their performance is scarce. In this paper, we show that a new family of classification trees, called dyadic classification trees (DCTs), are near optimal (in a minimax sense) for a very broad range of classification problems.This demonstrates that other schemes (e.g., neural networks, support vector machines) cannot perform significantly better than DCTs in many cases. We also show that this near optimal performance isattained with linear (in the number of training data) complexity growing and pruning algorithms. Moreover, the performance of DCTs on benchmark datasets compares favorably to that of standard CART, which is generally more computationally intensive and which does not possess similar near optimality properties. Our analysis stems from theoretical resultson structural risk minimization, on which the pruning rule for DCTs is based.

A/ D converters, What creates additional area and power overhead. The third row in Table 1 corresponds to the chip presented here.

Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved inthe space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithmspecialized for the Fisher score, which can exploit important dimensions.This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

We propose a framework for classifier design based on discriminative densities for representation of the differences of the class-conditional distributions ina way that is optimal for classification. The densities are selected from a parametrized set by constrained maximization of some objective function which measures the average (bounded) difference, i.e. the contrast between discriminative densities. We show that maximization ofthe contrast is equivalent to minimization of an approximation of the Bayes risk.

This paper introduces an algorithm for the automatic relevance determination ofinput variables in kernelized Support Vector Machines. Relevance is measured by scale factors defining the input space metric, and feature selection is performed by assigning zero weights to irrelevant variables. The metric is automatically tuned by the minimization of the standard SVM empirical risk, where scale factors are added to the usual set of parameters defining the classifier. Feature selection is achieved by constraints encouraging the sparsity of scale factors. The resulting algorithm compares favorably to state-of-the-art feature selection procedures anddemonstrates its effectiveness on a demanding facial expression recognition problem.

Echo state networks (ESN) are a novel approach to recurrent neural networktraining. An ESN consists of a large, fixed, recurrent "reservoir" network, from which the desired output is obtained by training suitable output connection weights. Determination of optimal outputweights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes anonline adaptation scheme based on the RLS algorithm known from adaptive linear systems. As an example, a 10th order NARMAsystem is adaptively identified.

However, what are the application areas include autonomous spacecraft key research topics? There are the scientific disciplines support, failure analysis and on-board diagnosis such as physics and chemistry that develop of vehicle systems, automated generation theories, and there are engineering disciplines of control software for photocopiers, and intelligent aids for learning about thermodynamic that develop solutions that change the cycles. Qualitative reasoning is thus relevant physical world. Both use formal mathematical for researchers who are interested in important systems, as well as computer implementations, AI issues as well as for managers, to derive conclusions about natural and artificial developers, and engineers who are looking for pieces of the world. Does this approach potential industrial benefits of AI. not provide a systematic and formal way to A decade has passed since the publication of reason about the physical world? What remains three collections of papers and a book covering to be done for AI research in this area?

In general, modeling is a complex and creative task, and building qualitative models is no exception. One way of automating this task is by means of machine learning. Observed behaviors of a modeled system are used as examples for a learning algorithm that constructs a model that is consistent with the data. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We describe the QUIN program that looks for qualitative patterns in numeric data and outputs the results of learning as "qualitative trees." We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations. The article then bridges to quantitative modeling, highlighting the benefits of qualitative reasoning-based approaches in the framework of system identification, and discusses open research issues.

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.