Machine Learning Proceedings 1995 PDF Download
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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: Armand Prieditis
Publisher: Morgan Kaufmann
ISBN: 1483298663
Category : Computers
Languages : en
Pages : 606
Get Book Here
Book Description
Machine Learning Proceedings 1995
Author: Stefan Wermter
Publisher: Springer Science & Business Media
ISBN: 9783540609254
Category : Computers
Languages : en
Pages : 490
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Book Description
This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.
Author: Peter Graf
Publisher: Springer Science & Business Media
ISBN: 9783540610403
Category : Computers
Languages : en
Pages : 308
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Book Description
Term indexing supports the construction of efficient automated reasoning systems, as e.g. automated theorem provers, by allowing rapid access to first order predicate calculus terms with specific properties. This monograph provides a comprehensive, well-written survey on term indexing in general and presents new indexing techniques for the retrieval and maintenance of data that help to overcome program degradation in automated reasoning systems. Theoretical foundations and applicational aspects are treated in detail; finally the PURR prover for parallel unit resulting resolution is discussed to demonstrate the importance of careful implementations.
Author: Uma N. Dulhare
Publisher: John Wiley & Sons
ISBN: 1119654742
Category : Computers
Languages : en
Pages : 544
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Book Description
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Author: Aboul Ella Hassanien
Publisher: Springer
ISBN: 3030023575
Category : Technology & Engineering
Languages : en
Pages : 472
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Book Description
The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
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: Claude Sammut
Publisher: Springer Science & Business Media
ISBN: 0387307680
Category : Computers
Languages : en
Pages : 1061
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Book Description
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Author: Olivier Sigaud
Publisher: John Wiley & Sons
ISBN: 1118620100
Category : Technology & Engineering
Languages : en
Pages : 367
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Book Description
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
Author: H. Chad Lane
Publisher: Springer Nature
ISBN: 3030584593
Category : Education
Languages : en
Pages : 664
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Book Description
This book constitutes the thoroughly refereed proceedings of the 11th International Conference on Computer Supported Education, CSEDU 2019, held in Heraklion, Crete, Greece, in May 2019. The 30 revised full papers were carefully reviewed and selected from 202 submissions. The papers cover wide research fields including authoring tools and content development, AV-communication and multimedia, classroom management, e-Learning hardware and software, blended learning, critical success factors in distance learning.
Author: Aske Plaat
Publisher: Springer Nature
ISBN: 9811906386
Category : Computers
Languages : en
Pages : 414
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Book Description
Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
