Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.

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Computational statistics shares two hallmarks with other “computational” sciences, such as computational physics, computational biology, and so on. One is a characteristic of the methodology: it is computationally intensive. The other is the nature of the tools of discovery.

Chapter 1 presents fundamental concepts from molecular biology. We describe the basicstructure and function of proteins and nucleic acids, the mechanisms of molecular genetics,the most important laboratory techniques for studying the genome of organisms, andan overview of existing sequence databases.Chapter 2 describes strings and graphs, two of the most important mathematical objectsused in the book. A brief exposition of general concepts of algorithms and theiranalysis is also given, covering definitions from the theory of NP-completeness.The following chapters are based on specific problems in molecular biology. Chapter3 deals with sequence comparison. The basic two-sequence problem is studied andthe classic dynamic programming algorithm is given. We then study extensions of thisalgorithm, which are used to deal with more general cases of the problem. A section is devotedto the multiple-sequence comparison problem. Other sections deal with programsused in database searches, and with some other miscellaneous issues.Chapter 4 covers the fragment assembly problem. This problem arises when a DNAsequence is broken into small fragments, which must then be assembled to reconstitutethe original molecule. This is a technique widely used in large-scale sequencing projects,such as the Human Genome Project. We show how various complications make thisproblem quite hard to solve. We then present some models for simplified versions of theproblem. Later sections deal with algorithms and heuristics based on these models.Chapter 5 covers the physical mapping problem. This can be considered as fragmentassembly on a larger scale. Fragments are much longer, and for this reason assemblytechniques are completely different. The aim is to obtain the location of some markersalong the original DNA molecule. A brief survey of techniques and models is given.We then describe an algorithm for th

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Computational methods in Finance, Hirsa。计算金融必备的入门书籍