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Calculus --- Calcul infinitésimal --- DIFFERENTIAL CALCULUS --- Calculus, Integral --- Mathematics --- Calcul differentiel --- Calcul intégral --- Problems, exercises, etc --- Outlines, syllabi, etc --- AA / International- internationaal --- 51 --- Wiskunde. --- Mathematical analysis --- Differential calculus --- Analyse mathématique --- Calcul différentiel --- Problems, exercises, etc. --- Problèmes et exercices --- Problèmes et exercices. --- Wiskunde --- Problèmes et exercices. --- Calculus - Problems, exercises, etc - Résumés, programmes --- Calcul infinitésimal - Problèmes et exercices --- Calculus - Outlines, syllabi, etc. --- Calculus - Problems, exercises, etc. --- Calculus - Problems, exercises, etc --- Calculus - Outlines, syllabi, etc

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AA / International- internationaal --- 50 --- Algemeenheden. Geschiedenis en filosofie van de wetenschappen. --- 3822 --- Gödel's theorem. --- Gödel, Kurt Friedrich, --- Logique mathématique --- Logic, Symbolic and mathematical --- Logique symbolique et mathématique --- --Logique mathématique --- --Logic, Symbolic and mathematical --- --Gödel's theorem. --- Algemeenheden. Geschiedenis en filosofie van de wetenschappen --- Calcul des prédicats --- Predicate calculus --- Boole, Algèbre de --- Algebra, Boolean --- Calcul des propositions --- Propositional calculus --- Fonctions récursives --- Recursive functions. --- Gödel, Théorème de --- Gödel's theorem. --- Théorie des ensembles --- Set theory. --- Théorie des modèles --- Model theory. --- Décidabilité (logique mathématique) --- Godel's theorem --- Logique mathématique --- Calcul des prédicats --- Boole, Algèbre de

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"Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier. Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks. The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning. Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. J. Stephen Judd is Visiting Assistant Professor of Computer Science at The California Institute of Technology. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman."

AI(Artificial intelligence) --- Artificial intelligence --- Artificial thinking --- Artificiële intelligentie --- Berekeningen [Ingewikkeldheid van ] --- Calcul [Complexité de ] --- Complexité de calcul --- Computational complexity --- Electronic brains --- Ingewikkeldheid van berekeningen --- Intellectronics --- Intelligence [Artificial ] --- Intelligence artificielle --- Intelligent machines --- Kunstmatige intelligentie --- Machine intelligence --- Neural computers --- Neurale computers --- Ordinateurs neuraux --- Thinking [Artificial ] --- 612.8 --- Neural computers. --- Neural net computers --- Neural network computers --- Neurocomputers --- Electronic digital computers --- Natural computation --- Complexity, Computational --- Electronic data processing --- Machine theory --- AI (Artificial intelligence) --- Intelligence, Artificial --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Zenuwstelsel. Zintuigen. Motorische neurowetenschappen --- Computational complexity. --- Artificial intelligence. --- Computer Science --- Engineering & Applied Sciences --- Cerveaux électroniques --- Machines intelligentes --- Pensée artificielle --- E-books --- COGNITIVE SCIENCES/General --- NEUROSCIENCE/General

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This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

Insurance --- -Linear models (Statistics) --- -368.01 --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Assurance (Insurance) --- Coverage, Insurance --- Indemnity insurance --- Insurance coverage --- Insurance industry --- Insurance protection --- Mutual insurance --- Underwriting --- Finance --- Mathematics --- -Electronic information resources --- Electronic information resources --- E-books --- Linear models (Statistics) --- Business mathematics --- Actuarial science --- Mathematics. --- Models lineals (Estadística) --- Assegurances --- Models matemàtics. --- Matemàtica. --- Mathematical Sciences --- Probability --- Càlcul actuarial --- Ciència actuarial --- Matemàtica actuarial --- Matemàtica financera --- Finances --- Finances privades --- Estadística --- Estadística matemàtica --- Models matemàtics

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Economics, Mathematical --- Calculus of variations --- 330.105 --- Wiskundige economie. Wiskundige methoden in de economie --- 330.105 Wiskundige economie. Wiskundige methoden in de economie --- 518.6 --- Wiskundige controlesystemen --- AA / International- internationaal --- 338.8 --- 330.3 --- 330.115 --- Economische groei. --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics. --- Kwantitatieve methoden (economie) --- Consumer behavior --- Quantitative methods (economics) --- Mathematical control systems --- Differential equations --- Calculus of variations. --- Economics, Mathematical. --- Optimisation mathématique. --- Calcul des variations. --- Mathématiques économiques. --- Mathematical optimization. --- Economische groei --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics --- Optimisation mathématique. --- Mathématiques économiques.