Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

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Pattern Recognition with Fuzzy Objective Function Algorithms

From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

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Fuzzy Set Theory - and Its Applications

This paper describes an experiment on the “linguistic” synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. The experiment was initiated to investigate the possibility of human interaction with a learning controller. However, the control strategy set up linguistically proved to be far better than expected in its own right, and the basic experiment of linguistic control synthesis in a non-learning controller is reported here.

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

The implementation architecture of a knowledge representation server as a class library in an object-oriented language is described. The graphic editing tools developed to support ease of interactive editing of the objects in the knowledge representation system are illustrated. It is shown that object-oriented paradigms are effective in knowledge representation for organizational modeling and problem solving. They allow system architectures to be developed that combine centralized repositories and distributed heterogeneous subsystems to support new forms of organizational integration.

Experience with a Class Library for Organizational Modeling and Problem Solving

This paper surveys the state-of-the-art in knowledge acquisition for knowledge-based systems. It gives an overview of three major areas of advance in recent years: in conceptual and theoretical terms, the characterization of knowledge acquisition as a process of modeling expertise with a view to emulating and extending it; in methodological terms, the provision of detailed formal modeling methodologies supporting such processes; and, in technological terms, the development of computer-based tools for knowledge acquisition supporting such modeling methodologies. The paper also presents the state-of-the-art in the context of its relation to other fields of activity such as developments in software engineering, system-theoretic aspects of modeling in general, and the variety of technologies that have been applied in knowledge acquisition such as those of hypermedia and machine learning.

Modeling and Extending Expertise

The training of human operators for skilled tasks may be regarded as the synthesis of a specific controller from a general-purpose adaptive device, by influencing its adaption through selection and variation of the learning environment. Selection of environments to maximise the rate of learning is itself a control problem, and an automatic feedback training system is proposed which feeds back information about the operator's performance to control the parameters of his environment. The stability and performance of the trainer have been investigated both theoretically and experimentally, and its utility has been tested in a fairly realistic training situation using as trainees both human operators and computer-simulated learning machines.

/pdf/training-the-human-adaptive-controller-1uet938ptb.pdf

Training the human adaptive controller

This paper describes a user modeling notation called the Annotated Flow Chart (AFC) that is highly intuitive and very easy for the domain experts to use. This notation is a form of “extended” flow chart. This notation is defined using Constraint Graphs. Modelling constructs represented in AFC is then mapped to MODEL-ECS (this is an executable conceptual modeling language based on Conceptual Graphs and Actors formalisms), to enable rapid prototyping of executable knowledge models. This paper describes the mappings between constructs in AFC and MODEL-ECS. An example in knowledge modeling is used to illustrate the application of AFC in rapid prototyping of knowledge models.

Brian R. Gaines

Papers

Experience with a Class Library for Organizational Modeling and Problem Solving

Modeling and Extending Expertise

Training the human adaptive controller

Knowledge Modeling Using Annotated Flow Chart

A Comparison of Books and Hypermedia for Knowledge-based Sports Coaching.