Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

How to Do Things With Words

Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between topdown expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: “[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the model's defining design principles. In listing the advantages of its THOT technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, “weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11],

Knowledge-Based Intelligent Information and Engineering Systems

Reasoning with incomplete and unreliable information is a central characteristic of decision making, for example in industry, medicine and finance. Bayesian networks provide a theoretical framework for dealing with this uncertainty using an underlying graphical structure and the probability calculus. Bayesian networks have been successfully implemented in areas as diverse as medical diagnosis and finance. We present a brief introduction to Bayesian networks for those readers new to them and give some pointers to the literature.

Introduction to Bayesian Networks

From the Publisher:
With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs - providing a summary of the design, applications, current research, and challenges of this dynamic and promising field.

Recurrent Neural Networks: Design and Applications

Very often real-world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms that combine two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, some fundamental concepts of multiobjective optimization are introduced, emphasizing the motivation and advantages of using evolutionary algorithms. We then lay out the important contributions of the remaining chapters of this volume.

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Evolutionary Multiobjective Optimization

Introduction to Fingerprint Recognition, U. Halici, L.C. Jain, and A. Erol Fingerprint Feature Processing Techniques and Poroscopy, A.R. Roddy and J.D. Stosz Fingerprint Sub-Classification: A Neural Network Approach, G.A. Drets and H.G. Leljecstroem A Gabor Filter-Based Method for Fingerprint Identification, Y. Hamamoto Minutiae Extraction and Filtering from Gray-Scale Images, D. Maio and D. Maltoni Feature Selective Filtering for Ridge Extraction, A. Erol, U. Halici, and G. Ongun Introduction to Face Recognition, A.J. Howell Neural Networks for Face Recognition, A.S. Pandya and R.R. Szabo Face Unit Radial Basis Function Networks, A.J. Howell Face Recognition from Correspondence Maps, R.P. Wurtz Face Recognition by Elastic Bunch Graph Matching, L. Wiskott, J.-M. Fellous, N. Kruger, and C. von der Malsburg Facial Expression Synthesis Using Radial Basis Function Networks, I. King and X.Q. Li Recognition of Facial Expressions and Its Application to Human Computer Interaction, T. Onisawa and S. Kitazake

Lakhmi C. Jain

Papers

Knowledge-Based Intelligent Information and Engineering Systems

Introduction to Bayesian Networks

Recurrent Neural Networks: Design and Applications

Evolutionary Multiobjective Optimization

Intelligent biometric techniques in fingerprint and face recognition