Machine Learning Proceedings 1994 PDF Download
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Author: William W. Cohen
Publisher: Morgan Kaufmann
ISBN: 1483298183
Category : Computers
Languages : en
Pages : 398
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Book Description
Machine Learning Proceedings 1994
Author: William W. Cohen
Publisher: Morgan Kaufmann
ISBN: 1483298183
Category : Computers
Languages : en
Pages : 398
Get Book Here
Book Description
Machine Learning Proceedings 1994
Author: J. Ross Quinlan
Publisher: Morgan Kaufmann
ISBN: 9781558602380
Category : Computers
Languages : en
Pages : 286
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Book Description
This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.
Author: William Cohen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Machine Learning Proceedings 1994.
Author: Armand Prieditis
Publisher: Morgan Kaufmann
ISBN: 1483298663
Category : Computers
Languages : en
Pages : 606
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Book Description
Machine Learning Proceedings 1995
Author: W. Bruce Croft
Publisher: Springer Science & Business Media
ISBN: 144712099X
Category : Computers
Languages : en
Pages : 371
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Book Description
Information retrieval (IR) is becoming an increasingly important area as scientific, business and government organisations take up the notion of "information superhighways" and make available their full text databases for searching. Containing a selection of 35 papers taken from the 17th Annual SIGIR Conference held in Dublin, Ireland in July 1994, the book addresses basic research and provides an evaluation of information retrieval techniques in applications. Topics covered include text categorisation, indexing, user modelling, IR theory and logic, natural language processing, statistical and probabilistic models of information retrieval systems, routing, passage retrieval, and implementation issues.
Author: Elzbieta Hajnicz
Publisher: Springer Science & Business Media
ISBN: 9783540609414
Category : Computers
Languages : en
Pages : 262
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Book Description
The notion of time plays an important role in modern science. In computer science and artificial intelligence, the parameter of time is of particular importance, e.g. for planning robot activity, natural language processing, and time-varying scene analysis. This work investigates the relationship between classic, first-order theories of point- and interval-based time structures, modal logics of corresponding structures, and their algorithmic representations. To make this relationship complete, a formalisation of Allen's famous algorithm, applicable to various structures of time, is presented along with its translation to modal logics. All in all, the book is a competent and comprehensive analysis of logical descriptions and algorithmic representations of time structures.
Author: Pradeep Kumar Mallick
Publisher: Springer Nature
ISBN: 3030481182
Category : Computers
Languages : en
Pages : 136
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Book Description
This edited book designs the Cognitive Computing in Human Cognition to analyze to improve the efficiency of decision making by cognitive intelligence. The book is also intended to attract the audience who work in brain computing, deep learning, transportation, and solar cell energy. Due to this in the recent era, smart methods with human touch called as human cognition is adopted by many researchers in the field of information technology with the Cognitive Computing.
Author: Zita A. Vale
Publisher: John Wiley & Sons
ISBN: 111983404X
Category : Technology & Engineering
Languages : en
Pages : 500
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Book Description
Intelligent Data Mining and Analysis in Power and Energy Systems A hands-on and current review of data mining and analysis and their applications to power and energy systems In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems, the editors assemble a team of distinguished engineers to deliver a practical and incisive review of cutting-edge information on data mining and intelligent data analysis models as they relate to power and energy systems. You’ll find accessible descriptions of state-of-the-art advances in intelligent data mining and analysis and see how they drive innovation and evolution in the development of new technologies. The book combines perspectives from authors distributed around the world with expertise gained in academia and industry. It facilitates review work and identification of critical points in the research and offers insightful commentary on likely future developments in the field. It also provides: A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods In-depth explorations of clustering, classification, and forecasting Intensive discussions of machine learning applications in power and energy systems Perfect for power and energy systems designers, planners, operators, and consultants, Intelligent Data Mining and Analysis in Power and Energy Systems will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.
Author: Sebastian Thrun
Publisher: Springer Science & Business Media
ISBN: 1461555299
Category : Computers
Languages : en
Pages : 346
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Book Description
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.
Author: Qingsheng Wang
Publisher: John Wiley & Sons
ISBN: 111981748X
Category : Technology & Engineering
Languages : en
Pages : 324
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Book Description
Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validation, and an overview of software and tools Detailed reviews of various applications in the areas of chemical safety and health, including flammability prediction, consequence prediction, asset integrity management, predictive nanotoxicity and environmental exposure assessment, and more Perspective on the possible future development of this field Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.
