Interactions In Artificial Intelligence And Statistical Methods Proceedings
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Interactions In Artificial Intelligence And Statistical Methods Proceedings
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Author : Robert Phelps
language : en
Publisher:
Release Date : 1989
Interactions In Artificial Intelligence And Statistical Methods Proceedings written by Robert Phelps and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with categories.
Interactions In Artificial Intelligence And Statistical Methods
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Author : Bob Phelps
language : en
Publisher: Gower Publishing Company, Limited
Release Date : 1987
Interactions In Artificial Intelligence And Statistical Methods written by Bob Phelps and has been published by Gower Publishing Company, Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Computers categories.
Artificial Intelligence And Statistics
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Author : William A. Gale
language : en
Publisher: Addison Wesley Publishing Company
Release Date : 1986
Artificial Intelligence And Statistics written by William A. Gale and has been published by Addison Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Computers categories.
A statistical view of uncertainty in expert systems. Knowledge, decision making, and uncertainty. Conceptual clustering and its relation to numerical taxonomy. Learning rates in supervised and unsupervised intelligent systems. Pinpoint good hypotheses with heuristics. Artificial intelligence approaches in statistics. REX review. Representing statistical computations: toward a deeper understanding. Student phase 1: a report on work in progress. Representing statistical knowledge for expert data analysis systems. Environments for supporting statistical strategy. Use of psychometric tools for knowledge acquisition: a case study. The analysis phase in development of knowledge based systems. Implementation and study of statistical strategy. Patterns in statisticalstrategy. A DIY guide to statistical strategy. An alphabet for statistician's expert systems.
Proceedings Of The Issek94 Workshop On Mathematical And Statistical Methods In Artificial Intelligence
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Author : G. Della Riccia
language : en
Publisher:
Release Date : 2014-09-01
Proceedings Of The Issek94 Workshop On Mathematical And Statistical Methods In Artificial Intelligence written by G. Della Riccia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-01 with categories.
Proceedings Of The Issek94 Workshop On Mathematical And Statistical Methods In Artificial Intelligence
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Author : G. Della Riccia
language : en
Publisher: Springer
Release Date : 1995-07-13
Proceedings Of The Issek94 Workshop On Mathematical And Statistical Methods In Artificial Intelligence written by G. Della Riccia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-07-13 with Computers categories.
In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of uncertain, incomplete or inconsistent information. Classical logic and probability theory are only partially adequate for this, and a variety of other formalisms have been developed, some of the most important being fuzzy methods, possibility theory, belief function theory, non monotonic logics and modal logics. The aim of this workshop was to contribute to the elucidation of similarities and differences between the formalisms mentioned above.
Intelligent Data Analysis
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Author : Michael R. Berthold
language : en
Publisher: Springer
Release Date : 2007-06-07
Intelligent Data Analysis written by Michael R. Berthold and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-07 with Computers categories.
This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
Games And Simulations To Enhance Quality Learning
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Author : Danny Saunders
language : en
Publisher: Psychology Press
Release Date : 1996
Games And Simulations To Enhance Quality Learning written by Danny Saunders and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Education categories.
Artificial Intelligence In Economics And Managment
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Author : Phillip Ein-Dor
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Artificial Intelligence In Economics And Managment written by Phillip Ein-Dor and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
In the past decades several researchers have developed statistical models for the prediction of corporate bankruptcy, e. g. Altman (1968) and Bilderbeek (1983). A model for predicting corporate bankruptcy aims to describe the relation between bankruptcy and a number of explanatory financial ratios. These ratios can be calculated from the information contained in a company's annual report. The is to obtain a method for timely prediction of bankruptcy, a so ultimate purpose called "early warning" system. More recently, this subject has attracted the attention of researchers in the area of machine learning, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This research is usually directed at the comparison of machine learning methods, such as induction of classification trees and neural networks, with the "standard" statistical methods of linear discriminant analysis and logistic regression. In earlier research, Feelders et al. (1994) performed a similar comparative analysis. The methods used were linear discriminant analysis, decision trees and neural networks. We used a data set which contained 139 annual reports of Dutch industrial and trading companies. The experiments showed that the estimated prediction error of both the decision tree and neural network were below the estimated error of the linear discriminant. Thus it seems that we can gain by replacing the "traditionally" used linear discriminant by a more flexible classification method to predict corporate bankruptcy. The data set used in these experiments was very small however.
Modelling For Added Value
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Author : Robert Macredie
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Modelling For Added Value written by Robert Macredie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
This book gathers together research from three key application themes of modelling in operational research - modelling to support evaluation and change in organisations; modelling within the development and use of organisational information systems; and the use of modelling approaches to support, enable and enhance decision support in organisational contexts. The issues raised provide valuable insight into the range of ways in which operational research techniques and practices are being successfully applied in today's information-centred business world. Modelling for Added Value provides a window onto current research and practise in modelling techniques and highlights their rising importance across the business, industrial and commercial sectors. The book contains contributions from a mix of academics and practitioners and covers a range of complex and diverse modelling issues, highlighting the broad appeal of this increasingly important subject area.
Statistics For Machine Learning
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Author : Himanshu Singh
language : en
Publisher: BPB Publications
Release Date : 2021-01-15
Statistics For Machine Learning written by Himanshu Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Computers categories.
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem Ê KEY FEATURESÊ _ Develop a Conceptual and Mathematical understanding of Statistics _ Get an overview of Statistical Applications in Python _ Learn how to perform Hypothesis testing in Statistics _ Understand why Statistics is important in Machine Learning _ Learn how to process data in Python Ê DESCRIPTIONÊÊ This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc.Ê You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. Ê WHAT YOU WILLÊ LEARNÊÊ _ Understand the basics of Statistics _ Get to know more about Descriptive Statistics _ Understand and learn advanced Statistics techniques _ Learn how to apply Statistical concepts in Python _ Understand important Python packages for Statistics and Machine Learning Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. TABLE OF CONTENTSÊ 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning