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A Compendium Of Machine Learning Symbolic Machine Learning
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Book Synopsis A Compendium of Machine Learning: Symbolic machine learning by : Garry Briscoe
Download or read book A Compendium of Machine Learning: Symbolic machine learning written by Garry Briscoe and published by Intellect (UK). This book was released on 1996 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
Book Synopsis Compendium of Neurosymbolic Artificial Intelligence by : P. Hitzler
Download or read book Compendium of Neurosymbolic Artificial Intelligence written by P. Hitzler and published by . This book was released on 2023-08-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines.This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time.Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.
Author :Artur S. d'Avila Garcez Publisher :Springer Science & Business Media ISBN 13 :1447102118 Total Pages :276 pages Book Rating :4.4/5 (471 download)
Book Synopsis Neural-Symbolic Learning Systems by : Artur S. d'Avila Garcez
Download or read book Neural-Symbolic Learning Systems written by Artur S. d'Avila Garcez and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Book Synopsis Neuro-Symbolic Artificial Intelligence: The State of the Art by : P. Hitzler
Download or read book Neuro-Symbolic Artificial Intelligence: The State of the Art written by P. Hitzler and published by IOS Press. This book was released on 2022-01-19 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.
Book Synopsis Machine Learning Control by Symbolic Regression by : Askhat Diveev
Download or read book Machine Learning Control by Symbolic Regression written by Askhat Diveev and published by Springer Nature. This book was released on 2021-10-23 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields. For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc. For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.
Download or read book Machine Learning written by and published by . This book was released on 1986 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning and Its Applications by : Georgios Paliouras
Download or read book Machine Learning and Its Applications written by Georgios Paliouras and published by Springer Science & Business Media. This book was released on 2001-08-01 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Book Synopsis Introduction to Symbolic Plan and Goal Recognition by : Reuth Reuth Mirsky
Download or read book Introduction to Symbolic Plan and Goal Recognition written by Reuth Reuth Mirsky and published by Springer Nature. This book was released on 2022-05-31 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author :Artur S. D'Avila Garcez Publisher :Springer Science & Business Media ISBN 13 :3540732454 Total Pages :200 pages Book Rating :4.5/5 (47 download)
Book Synopsis Neural-Symbolic Cognitive Reasoning by : Artur S. D'Avila Garcez
Download or read book Neural-Symbolic Cognitive Reasoning written by Artur S. D'Avila Garcez and published by Springer Science & Business Media. This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis Neuro Symbolic Reasoning and Learning by : Paulo Shakarian
Download or read book Neuro Symbolic Reasoning and Learning written by Paulo Shakarian and published by Springer Nature. This book was released on 2023-10-15 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.
Book Synopsis A Unifying Framework for Formal Theories of Novelty by : Terrance Boult
Download or read book A Unifying Framework for Formal Theories of Novelty written by Terrance Boult and published by Springer Nature. This book was released on 2023-08-01 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems. Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems. The book presents examples of using this framework to define and evaluate in multiple domains including image recognition image-based open world learning, hand-writing and author analysis, CartPole Control, Image Captioning, and Monopoly. Chapters are written by well-known contributors to this new and emerging field. In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games.
Book Synopsis Pattern Recognition and Machine Learning by : Y. Anzai
Download or read book Pattern Recognition and Machine Learning written by Y. Anzai and published by Morgan Kaufmann. This book was released on 1992-07-14 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognition and learning by a computer. Representing information. Generation and transformation of representations. Pattern feature extraction. Pattern understanding methods. Learning concepts. Learning procedures. Learning based on logic. Learning by classification and discovery. Learning by neural networks.
Book Synopsis Compendium of Neurosymbolic Artificial Intelligence by : P. Hitzler
Download or read book Compendium of Neurosymbolic Artificial Intelligence written by P. Hitzler and published by IOS Press. This book was released on 2023-08-04 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.
Book Synopsis Innovations in Machine Learning by : Dawn E. Holmes
Download or read book Innovations in Machine Learning written by Dawn E. Holmes and published by Springer. This book was released on 2006-02-28 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
Author :Ryszard Stanisław Michalski Publisher :Springer Science & Business Media ISBN 13 :9783540132981 Total Pages :572 pages Book Rating :4.1/5 (329 download)
Book Synopsis Machine Learning by : Ryszard Stanisław Michalski
Download or read book Machine Learning written by Ryszard Stanisław Michalski and published by Springer Science & Business Media. This book was released on 1984-01 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt:
