Bøger af Mikhail Moshkov

In this book, we study decision trees for fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions. We consider two main cases: when the scheme (circuit or switching network) has the same mode of operation for both calculation and diagnostics, and when the scheme has two modes of operation¿normal for calculation and special for diagnostics. In the former case, we get mostly negative results, including superpolynomial lower bounds on the minimum depth of diagnostic decision trees depending on scheme complexity and the NP-hardness of construction diagnostic decision trees. In the latter case, we describe classes of schemes and types of faults for which decision trees can be effectively used to diagnose schemes, when they are transformed into so-called iteration-free schemes.The tools and results discussed in this book help to understand both the possibilities and challenges of using decision trees to diagnosefaults in various schemes. The book is useful to specialists both in the field of theoretical and technical diagnostics.It can also be used for the creation of courses for graduate students.

The results presented here (including the assessment of a new tool - inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions.

This book covers Test Theory, Rough Sets and Logical Analysis of Data. The authors intend for this comparative appraisal to lead to new theoretical results and real-life applications based on the combination of the three approaches.

Dynamic programming is an efficient technique for solving optimization problems. and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation.