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| 005 | 20211213124110.0 | ||
| 008 | 740719s1975 nyua b 00110 engm | ||
| 010 | _a74001626 //r78 | ||
| 020 | _a0123623405 | ||
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_aDLC _cDLC _dCWR _dm.c _dEG-NcFUE _erda |
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| 050 | 0 |
_aQ335 _b.H79 1975 |
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| 082 | 0 | 4 |
_a001.535 _222 _bM.R.A |
| 100 | 1 | 0 | _aMishra, Ravi Bhushan |
| 245 | 0 | 0 |
_aArtificial intelligence _cBy Ravi Bhushan Mishra. |
| 264 | 1 |
_aNew Delhi : _bOhl Learning, _c2011. |
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| 300 |
_axii, 503 pages ; _billustration : _c24 cm. |
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| 336 |
_2rdacontent _atext |
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| 337 |
_2rdamedia _aunmediated |
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| 338 |
_2rdacarrier _avolume |
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| 490 | 0 | _aAcademic Press series in cognition and perception. | |
| 504 | _aBibliography: p. [446]-457. | ||
| 505 | 0 | _a Front Cover; Artificial Intelligence; Copyright Page; Table of Contents; PREFACE; ACKNOWLEDGMENTS; Part I: Introduction; Chapter I. THE SCOPE OF ARTIFICIAL INTELLIGENCE; 1.0 IS THERE SUCH A THING?; 1.1 Problem Solving; 1.2 Pattern Recognition; 1.3 Game Playing and Decision Making; 1.4 Natural Language and Machine Comprehension; 1.5 Self-Organizing Systems; 1.6 Robotology; Chapter II. PROGRAMMING, PROGRAM STRUCTURE, AND COMPUTABILITY; 2.0 The Relevance of Computability; 2.1 Computations on Strings; 2.2 Formal Grammars; 2.3 Turing Machines; 2.4 Linear Bounded Automata and Type 1 Languages. 2.5 Pushdown Automata and Type 2 Languages2.6 Finite Automata and Regular (Type 3) Languages; 2.7 Summary and Comments on Practicality; Part II: Pattern Recognition; Chapter III. GENERAL CONSIDERATIONS IN PATTERN RECOGNITION; 3.0 Classification; 3.1 Categorizing Pattern-Recognition Problems; 3.2 Historical Perspective and Current Issues; Chapter IV. PATTERN CLASSIFICATION AND RECOGNITION METHODS BASED ON EUCLIDEAN DESCRIPTION SPACES; 4.0 General; 4.1 . Bayesian Procedures in Pattern Recognition; 4.2 Classic Statistical Approach to Pattern Recognition and Classification. 4.3 Classification Based on Proximity of Descriptions4.4 Learning Algorithms; 4.5 Clustering; Chapter V. NON-EUCLIDEAN PARALLEL PROCEDURES: THE PERCEPTRON; 5.0 Introduction and Historical Comment; 5.1 Terminology; 5.2 Basic Theorems for Order-Limited Perceptrons; 5.3 Substantive Theorems for Order-Limited Perceptrons; 5.4 | |
| 505 | 0 | _aCapabilities of Diameter-Limited Perceptrons; 5.5 The Importance of Perceptron Analysis; Chapter VI. SEQUENTIAL PATTERN RECOGNITION; 6.0 Sequential Classification; 6.1 Definitions and Notation; 6.2 Bayesian Decision Procedures. 6.3 Bayesian Optimal Classification Procedures Based on Dynamic Programming6.4 Approximations Based on Limited Look Ahead Algorithms; 6.5 Convergence in Sequential Pattern Recognition; Chapter VII. GRAMMATICAL PATTERN CLASSIFICATION; 7.0 The Linguistic Approach to Pattern Analysis; 7.1 The Grammatical Inference Problem; 7.2 Grammatical Analysis Applied to Two-Dimensional Images; Chapter VIII. FEATURE EXTRACTION; 8.0 General; 8.1 Formalization of the Factor-Analytic Approach; 8.2 Formalization of the Binary Measurement Case; 8.3 Constructive Heuristics for Feature Detection. 8.4 An Experimental Study of Feature Generation in Pattern Recognition8.5 On Being Clever; Part III: Theorem Proving and Problem Solving; Chapter IX. COMPUTER MANIPULABLE REPRESENTATIONS IN PROBLEM SOLVING; 9.0 The Use of Representations; 9.1 A Typology of Representations; 9.2 Combining Representations; Chapter X. GRAPHIC REPRESENTATIONS IN PROBLEM SOLVING; 10.0 Basic Concepts and Definitions; 10.1 Algorithms for Finding a Minimal Path to a Single Goal Node; 10.2 An ""Optimal"" Ordered Search Algorithm; 10.3 Tree Graphs and Their Use; Chapter XI. HEURISTIC PROBLEM-SOLVING PROGRAMS. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 2 | _aComputers. | |
| 650 | 2 | _aCybernetics. | |
| 856 |
_3Abstract _uhttp://repository.fue.edu.eg/xmlui/handle/123456789/3480 |
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