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| Author | : Society for Artificial Intelligence and Statistics |
| Publisher | : |
| Total Pages | : 541 |
| Release | : 1995 |
| Genre | : Artificial intelligence |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 546 |
| Release | : 1993 |
| Genre | : Artificial intelligence |
| ISBN | : |
| Author | : Usama M. Fayyad |
| Publisher | : |
| Total Pages | : 364 |
| Release | : 1995 |
| Genre | : Computers |
| ISBN | : |
| Author | : Maarten van Someren |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 380 |
| Release | : 1997-04-09 |
| Genre | : Computers |
| ISBN | : 9783540628583 |
This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.
| Author | : Doug Fisher |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 444 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461224047 |
Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.
| Author | : David E. Heckerman |
| Publisher | : Morgan Kaufmann Publishers |
| Total Pages | : 332 |
| Release | : 1999 |
| Genre | : Artificial intelligence |
| ISBN | : |
| Author | : Alexander Bird |
| Publisher | : Routledge |
| Total Pages | : 793 |
| Release | : 2013 |
| Genre | : Philosophy |
| ISBN | : 0415492297 |
This title offers a selection of thought-provoking articles that examine a broad range of issues, from the demarcation problem, induction and explanation to contemporary issues such as the relationship between science and race and gender, and science and religion
| Author | : Glenn Shafer |
| Publisher | : MIT Press |
| Total Pages | : 554 |
| Release | : 1996 |
| Genre | : Computers |
| ISBN | : 9780262193689 |
In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy. The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences -- medicine, business, engineering, and artificial intelligence -- must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences. Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.
| Author | : Matthias Dehmer |
| Publisher | : John Wiley & Sons |
| Total Pages | : 438 |
| Release | : 2008-09-08 |
| Genre | : Medical |
| ISBN | : 3527622829 |
This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.
| Author | : Pedro Larrañaga |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 398 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461515394 |
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.
