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Summary We present a tutorial introduction to Support Vector Machines (SVM) and try to show using intuitive arguments as to why SVMs tend to perform so well on a variety of challenging problems. We then discuss the quadratic optimization problem that arises as a result of the SVM formulation. We talk about a few computationally cheaper alternative formulations that have been developed in recent years...
Summary Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and description of dynamic systems by Kalman filters and difference equations are well known since many years, Bayesian networks have not been applied to problems in...
Summary AppART is an adaptive resonance theory low parameterized neural model that incrementally approximates continuous—valued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higher—order Nadaraya—Watson regression and can be interpreted as a fuzzy logic standard additive model. In this chapter we describe AppART dynamics and training. We discuss...
Summary The Rough Set Theory (RST) is a mathematical formalism for representing uncertainty, which can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge based systems. This chapter introduces the main concepts of the RST and presents a family of algorithms...
Summary Knowledge acquisition for a case-based reasoning system from domain experts is a bottleneck in the system development process. It would be useful to derive representative cases automatically from larger, available databases rather than acquiring them from domain experts. Case selection is a branch of data mining that aims at choosing representative cases from large data sets for future case-based...
Summary In this chapter we introduce a new version of the machine learning algorithm, FDM, based on a new notion of the fuzzy derivative. The main idea is to describe the influence of the change of one parameter on another. In this algorithm we generate sets of classification rules. We define a coefficient of significance for every single rule. This coefficient describes for a test example a degree...
Summary In this chapter, we present declarative, model, and fixpoint semantics for fuzzy disjunctive programs with weak similarity — sets of graded strong literal disjunctions. We shall suppose that truth values constitute a complete Boolean lattice L = (L, ≤, ∪, ∩, ⇒, 0, 1). A graded strong literal disjunction is a pair (D, c) where D is a strong literal disjunction of the form % MathType!MTEF!2!1!+-...
Summary To successfully prepare and model data, the data miner needs to be aware of the properties of the data manifold. In this chapter, the outline of a tool for automatically generating data survey reports for this purpose is described. Such a report is used as a starting point for data understanding, acts as documentation of the data, and can easily be redone if necessary. The main focus is on...
Summary We introduce a new form of Grammar-Guided Genetic Programming using Tree Adjunct Grammars instead of Context Free Grammars. We apply it to a standard problem of finding trigonometric identities, and compare its performance with standard approaches. We analyze the fitness landscape of the problem, and gain some understanding of the relative performance of the standard and new representations.
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