Items related to Advances in Information Systems Science: Volume 1
Synopsis
1 Theory of Algorithms and Discrete Processors.- 1. Introduction.- 2. Discrete Processors.- 3. Examples of Discrete Processors.- 3.1. Turing Machines.- 3.2. Markov's Normal Algorithms.- 3.3. Kaluzhnin's Graph Schemata and Logical Schemata of Algorithms.- 3.4. Programs in Algorithmic Languages.- 4. Computers and Discrete Processors.- 5. Systems of Algorithmic Algebras.- 6. Application of Algorithmic Algebras to Transformations of Microprograms.- 7. Equivalence of Discrete Processors.- 8. Equivalence of Automata with Terminal States Relative to an Automaton without Cycles.- 9. Specific Cases of Solutions to the Equivalence Problem.- 10. Conclusions.- References.- 2 Programming Languages.- 1. Introduction.- 2. The Basic Linguistic Nature of Programming Languages.- 2.1. Language and Communication.- 2.2. The Necessity of Rigor.- 2.3. Programs and Jobs.- 3. Programming Languages and Semiotics.- 3.1. The Three Branches of Semiotics.- 3.2. Programming Languages and Programming Systems.- 4. The Formal Definition of Programming Lan guages.- 4.1. Syntax.- 4.2. The Role of Declarations. Languages and Linguistic Systems.- 4.3. Semantics and Pragmatics.- 5. The Definition of Programmable Automata and their Languages.- 6. Parallel Concurrent Processes.- 7. Machine Languages.- 7.1. Direct Machine Languages.- 7.2. Symbolic Machine Languages.- 8. Special and General-Purpose Algorithmic Languages.- 8.1. Numerical Algorithmic Languages.- 8.2. Commercial and File Processing Languages.- 8.3. Symbol Manipulation Languages.- 8.4. General-Purpose Algorithmic Languages..- 9. Special Problem-Oriented Languages.- 9.1. Problem-Defining Languages.- 9.2. Programming Languages for Numerically Controlled Machines.- 9.3. Picture Manipulation Languages.- 10. Simulation Languages.- 10.1. Simulation Languages and Dynamical Systems.- 10.2. Discrete Simulation Languages.- 10.3. Continuous Simulation Languages.- 11. Conversational Languages.- 12. Conclusion.- References.- 3 Formula Manipulation-The User's Point of View.- 1. Introduction.- 1.1. The Nature of Formula Manipulation.- 2. Different Types of Formula Manipulation Systems.- 2.1. Polynomials and Rational Functions ....- 2.2. Analytical Methods.- 2.3. Definitional Facilities.- 2.4. Interactive Systems and Methods.- 3. Toward a Mathematical Utility.- 4. The Formula Manipulation Language Symbal.- 5. The Syntax of Symbal.- 5.1. The Basic Symbols.- 5.2. The Basic Syntactic Elements.- 5.3. Expressions.- 5.4.Vectors.- 5.5. Statements and the Block Structure.- 5.6. Quotations.- 6. The Basic Symbols and Syntactic Entities.- 6.1. Variables, Types, and Values.- 6.2. The Structure of Values.- 7. Expressions.- 7.1. Differentiation and Substitution.- 7.2. The Evaluation of Expressions.- 7.3. The For Clause.- 7.4. The Operators for Sums and Products...- 7.5. The Power of Expressions.- 8. The Remaining Parts of the Language.- 8.1. Vectors.- 8.2. Procedures.- 8.3. Statements and the Block Structure.- 9. Standard Variables.- 9.1. The Modes of Symbal.- 9.2. Control of Simplification.- 9.3. Control of Output.- 10. Techniques and Applications.- 10.1. Numerical Problems.- 10.2. Polynomials and Power Series.- 10.3. Differential Equations.- 10.4. Linear Algebra.- 11. Summary.- References.- 4 Engineering Principles of Pattern Recognition.- 1. Introduction.- 2. Basic Problems in Pattern Recognition.- 3. Feature Selection and Preprocessing.- 3.1. Probability Density Functions.- 3.2. Feature Selection Through Entropy Minimization.- 3.3. Feature Extraction Through Functional Approximation.- 4. Pattern Classification by Distance Functions.- 4.1. Categories Representable by Standard Patterns.- 4.2. Categories Not Representable by Standard Patterns.- 4.3. Realization of Linear Decision Functions.- 4.4. General Decision Functions.- 4.5. Training Algorithms.- 5. Pattern Classification by Potential Functions...- 5.1. Generation of Decision Functions.- 5.2. Geometrical Interpretation and Weight Adjustment.- 5.3. Convergence of Training Algor
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