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[null,"1.","Record Nr.",null,"UNINA9910780894203321"]
[null,null,"Autore",null,"Baerns M (Manfred), <1934->"]
[null,null,"Titolo",null,"Combinatorial development of solid catalytic materials [[electronic resource] ] : design of high-throughput experiments, data analysis, data mining / / Manfred Baerns, Martin Holeňa"]
[null,null,"Pubbl/distr/stampa",null,"London, : Imperial College Press, c2009"]
[null,null,"ISBN",null,"1-282-75985-X"]
[{"$nulls":4},"9786612759857"]
[{"$nulls":4},"1-84816-344-4"]
[null,null,"Descrizione fisica",null,"1 online resource (191 p.)"]
[null,null,"Collana",null,"Catalytic science series ; ; v. 7"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Catalysis - Computer simulation"]
[{"$nulls":4},"Catalysis - Mathematical models"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Contents; Dedication; Preface; Chapter 1. Background of Combinatorial Catalyst Development (M. Baerns); Bibliography; Chapter 2. Approaches in the Development of Heterogeneous Catalysts (M. Baerns); 2.1. Fundamental Aspects; 2.2. High-throughput Technologies for Preparation and Testing in Combinatorial Development of Catalytic Materials; 2.2.1. Selection of Potential Elements for Defining the Multi-parameter Compositional Space of Catalytic Materials; 2.2.2. Experimental Tools for Preparing and Testing Large Numbers of Catalytic-material Specimens; 2.2.2.1. Preparation of catalytic materials"]
[{"$nulls":4},"2.2.2.2. Testing and screening of catalytic materialsBibliography; Chapter 3. Mathematical Methods of Searching for Optimal Catalytic Materials (M. Holena); 3.1. Introduction; 3.2. Statistical Design of Experiments; 3.3. Optimisation Methods for Empirical Objective Functions; 3.4. Evolutionary Optimisation: The Main Approach to Seek Optimal Catalysts; 3.4.1. Dealing with Constraints in Genetic Optimisation; 3.5. Other Stochastic Optimisation Methods; 3.6. Deterministic Optimisation; 3.6.1. Utilizability of Methods with Derivatives in Catalysis; Bibliography"]
[{"$nulls":4},"Chapter 4. Generating Problem-Tailored Genetic Algorithms for Catalyst Search (M. Holena)4.1. Using a Program Generator - Why and How; 4.2. Description Language for Optimisation Tasks in Catalysis; 4.3. Tackling Constrained Mixed Optimisation; 4.4. A Prototype "]
[{"$nulls":4},"Implementation; Bibliography; Chapter 5. Analysis and Mining of Data Collected in Catalytic Experiments (M. Holena); 5.1. Similarity and Difference Between Data Analysis and Mining; 5.2. Survey of Existing Methods; 5.2.1. Statistical Methods; 5.2.2. Extraction of Logical Rules from Data; 5.3. Case Study with the Synthesis of HCN"]
[{"$nulls":4},"BibliographyChapter 6. Artificial Neural Networks in the Development of Catalytic Materials (M. Holena); 6.1. What are Artificial Neural Networks?; 6.1.1. Network Architecture; 6.1.2. Important Kinds of Neural Networks; 6.1.3. Activity of Neurons; 6.1.4. What do Neural Networks Compute?; 6.2. Approximation Capability of Neural Networks; 6.3. Training Neural Networks; 6.4. Knowledge Obtainable from a Trained Network; Bibliography; Chapter 7. Tuning Evolutionary Algorithms with Artificial Neural Networks (M. Holena); 7.1. Heuristic Parameters of Genetic Algorithms"]
[{"$nulls":4},"7.2. Parameter Tuning Based on Virtual Experiments7.3. Case Study with the Oxidative Dehydrogenation of Propane; Bibliography; Chapter 8. Improving Neural Network Approximations (M. Holena); 8.1. Importance of Choosing the Right Network Architecture; 8.2. Influence of the Distribution of Training Data; 8.3. Boosting Neural Networks; 8.4. Case Study with HCN Synthesis Continued; Bibliography; Chapter 9. Applications of Combinatorial Catalyst Development and An Outlook on Future Work (M. Baerns); 9.1. Introduction; 9.2. Experimental Applications of Combinatorial Catalyst Development"]
[{"$nulls":4},"9.3. Methodology"]
[null,null,null,"Sommario/riassunto","The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis."]
[null,"2.","Record Nr.",null,"UNINA9910830702503321"]
[null,null,"Autore",null,"Clark Alexander (Alexander Simon)"]
[null,null,"Titolo",null,"Linguistic nativism and the poverty of the stimulus [[electronic resource] /] / Alex Clark and Shalom Lappin"]
[null,null,"Pubbl/distr/stampa",null,"Malden, MA, : Wiley-Blackwell, 2010"]
[null,null,"ISBN",null,"1-283-51427-3"]
[{"$nulls":4},"9786613826725"]
[{"$nulls":4},"1-4443-9056-2"]
[{"$nulls":4},"1-4443-9054-6"]
[null,null,"Descrizione fisica",null,"1 online resource (262 p.)"]
[null,null,"Disciplina",null,"401.93"]
[{"$nulls":4},"401/.93"]
[null,null,"Soggetti",null,"Language acquisition"]
[{"$nulls":4},"Native language"]
[{"$nulls":4},"Computational linguistics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Linguistic Nativism and the Poverty of the Stimulus; Contents; Preface; 1 Introduction: Nativism in Linguistic Theory; 2 Clarifying the Argument from the Poverty of the Stimulus; 3 The Stimulus: Determining the Nature of Primary Linguistic Data; 4 Learning in the Limit: The Gold Paradigm; 5 Probabilistic Learning Theory for Language Acquisition; 6 A Formal Model of Indirect Negative Evidence; 7 Computational Complexity and Efficient Learning; 8 Positive Results in Efficient Learning; 9 Grammar Induction through Implemented Machine Learning"]
[{"$nulls":4},"10 Parameters in Linguistic Theory and Probabilistic Language Models11 A Brief Look at Some Biological and Psychological Evidence; 12 Conclusion; References; Author Index; Subject Index"]
[null,null,null,"Sommario/riassunto","This unique contribution to the ongoing discussion of language acquisition considers the Argument from the Poverty of the Stimulus in language learning in the context of the wider debate over cognitive, computational, and linguistic issues. Critically examines the Argument from the Poverty of the Stimulus - the theory that the linguistic input which children receive is insufficient to explain the rich and rapid development of their knowledge of their first language(s) through general learning mechanismsFocuses on formal learnability properties of the class of natural languages, con"]