Resultados de búsqueda - Learning algorithms

Connectedness, engagement, and learning through social work communities on LinkedIn / por Castillo-de Mesa, Joaquín

Enlace del recurso
Ver en el OPAC

Bio-inspired models for natural and formal languages

Tabla de Contenidos: “…Experiments using semantics for learning language comprehension and production /…”
Enlace del recurso
Ver en el OPAC

Proceedings of the 20th. Conference of the International Group for the Psychology of Mathematics Education: Vol.2 / por Puig, Luis

Ver en el OPAC

Bioinformatics and molecular evolution / por Higgs, Paul G.

Tabla de Contenidos: “…The revolution in biological information -- Nucleic acids, proteins, and amino acids -- Molecular evolution and population genetics -- Models of sequence evolution -- Information resources for genes and proteins -- Sequence alignment algorithms -- Searching sequence databases -- Phylogenetic methods -- Patterns in protein families -- Probabilistic methods and machine learning -- Further topics in molecular evolution and phylogenetics -- Genome evolution -- DNA microarrays and the 'omes.…”
Enlace del recurso
Ver en el OPAC

Digital signal processsing using MATLAB for students and researchers / por Leis, John, 1966-

Enlace del recurso
Ver en el OPAC

A web-based approach to measure skill mismatches and skills profiles for a developing country : the case of Colombia / por Cárdenas Rubio, Jeisson

Tabla de Contenidos: “…CONCLUSIONS -- REFERENCES -- APPENDIX -- APPENDIX A: EXAMPLES OF JOB PORTAL STRUCTURES -- APPENDIX B: TEXT MINING -- APPENDIX C: DETAILED PROCESS DESCRIPTION FOR THE CLASSIFICATION OF COMPANIES -- APPENDIX D: MACHINE LEARNING ALGORITHMS -- APPENDIX E: SUPPORT VECTOR MACHINE (SVM) -- APPENDIX F: SVM USING JOB TITLES -- APPENDIX G: NEAREST NEIGHBOUR ALGORITHM USING JOB TITLES -- APPENDIX H: ADDITIONAL TABLES.…”
Enlace del recurso
Ver en el OPAC

Multicriteria decision aid and artificial intelligence : links, theory and applications / por Doumpos, Michael

Tabla de Contenidos: “…s Method 7.5 Discussion 7.6 Conclusion References Part Four Multiobjective Optimization 8 Interactive Approaches Applied to Multiobjective Evolutionary Algorithms 8.1 Introduction 8.1.1 Methods Analyzed in this Chapter 8.2 Basic Concepts and Notation 8.2.1 Multiobjective Optimization Problems 8.2.2 Classical Interactive Methods 8.3 MOEAs Based on Reference Point Methods 8.3.1 A Weighted Distance Metric 8.3.2 Light Beam Search Combined with NSGA-II 8.3.3 Controlling the Accuracy of the Pareto Front Approximation 8.3.4 Light Beam Search Combined with PSO 8.3.5 A Preference Relation Based on a Weighted Distance Metric 8.3.6 The Chebyshev Preference Relation 8.4 MOEAs Based on Value Function Methods 8.4.1 Progressive Approximation of a Value Function 8.4.2 Value Function by Ordinal Regression 8.5 Miscellaneous Methods 8.5.1 Desirability Functions 8.6 Conclusions and Future Work References 9 Generalized DEA and Computational Intelligence in Multiple Criteria Decision Making 9.1 Introduction 9.2 Generalized Data Envelopment Analysis 9.2.1 Basic DEA Models: CCR, BCC and FDH Models 9.2.2 GDEA Model 9.3 Generation of Pareto Optimal Solutions using Generalized DEA and Computational Intelligence 9.3.1 GDEA in Fitness Evaluation 9.3.2 GDEA in Deciding the Parameters of Multi-objective PSO 9.3.3 Expected Improvement for Multi-objective Optimization Using GDEA 9.4 Summary References 10 Fuzzy Multiobjective Optimization 10.1 Introduction 10.2 Solution Concepts for Multiobjective Programming 10.3 Interactive Multiobjective Linear Programming 10.4 Fuzzy Multiobjective Linear Programming 10.5 Interactive Fuzzy Multiobjective Linear Programming 10.6 Interactive Fuzzy Multiobjective Linear Programming with Fuzzy Parameters 10.7 Interactive Fuzzy Stochastic Multiobjective Linear Programming 10.8 Related Works and Applications References Part Five Applications in Management and Engineering 11 MCDA & Agents: Supporting Effective Resource Federation in Virtual Organizations 11.1 Introduction 11.2 The Intuition of Multiple Criteria Decision Aid in Multi-agent Systems 11.3 Resource Federation Applied 11.3.1 Describing the Problem in a Cloud Computing Context 11.3.2 Problem Modeling 11.3.3 Assessing Agents' Value Function for Resource Federation 11.4 An Illustrative Example 11.5 Conclusions References 12 Fuzzy AHP Using Type II Fuzzy Sets: An Application to Warehouse Location Selection 12.1 Introduction 12.2 Multicriteria Selection 12.2.1 The ELECTRE (Elimination Et Choix Traduisant la Realite) Method 12.2.2 PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) 12.2.3 TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) 12.2.4 The WSM (Weighted Sum Model) Method 12.2.5 MAUT (Multi-attribute Utility Theory) 12.2.6 AHP (Analytic Hierarchy Process) 12.3 Literature Review on Fuzzy AHP 12.4 Buckley's Type-1 Fuzzy AHP 12.5 Type-2 Fuzzy Sets 12.6 Type-2 Fuzzy AHP 12.7 An Application: Warehouse Location Selection 12.8 Conclusion References 13 Applying Genetic Algorithms to Optimize Energy Efficiency in Buildings 13.1 Introduction 13.2 State-of-the-Art Review 13.3 An Example Case Study 13.3.1 Basic Principles and Problem Definition 13.3.2 Decision Variables 13.3.3 Decision Criteria 13.3.4 Decision Model 13.4 Development and Application of a Genetic Algorithm for the Example Case Study 13.4.1 Development of the Genetic Algorithm 13.4.2 Application of the Genetic Algorithm, Analysis of Results and Discussion 13.5 Conclusions References 14 Nature-Inspired Intelligence for Pareto Optimality Analysis in Portfolio Optimization 14.1 Introduction 14.2 Literature Review 14.3 Methodological Issues 14.4 Pareto Optimal Sets in Portfolio Optimization 14.4.1 Pareto Efficiency 14.4.2 Mathematical Formulation of the Portfolio Optimization Problem 14.5 Computational Results 14.5.1 Experimental Setup 14.5.2 Efficient Frontier 14.6 Conclusion References Index.…”
Enlace del recurso
Ver en el OPAC

Oversimplifying Teaching of the Control of Variables Strategy /

Enlace del recurso
Ver en el OPAC