Zero Error Margin
Author: Des Barker
Publisher:
Published: 2003
Total Pages: 344
ISBN-13:
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Algorithmic Learning Theory
Author: Nader H. Bshouty
Publisher: Springer
Published: 2012-10-01
Total Pages: 391
ISBN-13: 3642341063
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.
A Guide to Zero Defects
Author: United States. Office of the Assistant Secretary of Defense (Installations and Logistics)
Publisher:
Published: 1965
Total Pages: 32
ISBN-13:
DOWNLOAD EBOOKAlgorithmic Learning Theory
Author: Shai Ben David
Publisher: Springer Science & Business Media
Published: 2004-09-23
Total Pages: 519
ISBN-13: 3540233563
DOWNLOAD EBOOKAlgorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
The Meta-Analytic Organization
Author: Lex Donaldson
Publisher: Routledge
Published: 2015-03-26
Total Pages: 317
ISBN-13: 1317455800
DOWNLOAD EBOOKThe Meta-Analytic Organization: Introducing Statistico-Organizational Theory develops new organizational theory based upon ideas from statistics and methodology. There have been previous organizational theories based on academic disciplines such as biology, economics, and sociology. Statistico-organizational theory uniquely constructs a new organizational theory derived from ideas in statistics and psychometrics. The core idea is that errors known to occur in social science research must also occur when managers look at their data and seek to make inferences about cause and effect. Statistico-organizational theory uses methodological principles to predict when errors will occur and how great they will be. The book offers new theoretical propositions about organizational strategy and structure, human resource management, international business and franchising.
Efficiency and Scalability Methods for Computational Intellect
Author: Igelnik, Boris
Publisher: IGI Global
Published: 2013-04-30
Total Pages: 370
ISBN-13: 1466639431
DOWNLOAD EBOOKComputational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.
Fundamentals of Computation Theory
Author: R. V. Freĭvald
Publisher: Springer Science & Business Media
Published: 2001-08-03
Total Pages: 554
ISBN-13: 3540424873
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 13th International Symposium Fundamentals of Computation Theory, FCT 2001, as well as of the International Workshop on Efficient Algorithms, WEA 2001, held in Riga, Latvia, in August 2001. The 28 revised full FCT papers and 15 short papers presented together with six invited contributions and 8 revised full WEA papers as well as three invited WEA contributions have been carefully reviewed and selected. Among the topics addressed are a broad variety of topics from theoretical computer science, algorithmics and programming theory. The WEA papers deal with graph and network algorithms, flow and routing problems, scheduling and approximation algorithms, etc.
Learning from Data
Author: Vladimir Cherkassky
Publisher: John Wiley & Sons
Published: 2007-09-10
Total Pages: 560
ISBN-13: 9780470140512
DOWNLOAD EBOOKAn interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publisher: Springer
Published: 2017-07-01
Total Pages: 462
ISBN-13: 3319624164
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
Fundamentals of Computation Theory
Author: Rusins Freivalds
Publisher: Springer
Published: 2003-05-15
Total Pages: 554
ISBN-13: 3540446699
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 13th International Symposium Fundamentals of Computation Theory, FCT 2001, as well as of the International Workshop on Efficient Algorithms, WEA 2001, held in Riga, Latvia, in August 2001. The 28 revised full FCT papers and 15 short papers presented together with six invited contributions and 8 revised full WEA papers as well as three invited WEA contributions have been carefully reviewed and selected. Among the topics addressed are a broad variety of topics from theoretical computer science, algorithmics and programming theory. The WEA papers deal with graph and network algorithms, flow and routing problems, scheduling and approximation algorithms, etc.
