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Learning And Generalisation
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Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :1447137485 Total Pages :498 pages Book Rating :4.4/5 (471 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :9781852333737 Total Pages :520 pages Book Rating :4.3/5 (337 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2002-09-27 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Book Synopsis Experience, Variation and Generalization by : Inbal Arnon
Download or read book Experience, Variation and Generalization written by Inbal Arnon and published by John Benjamins Publishing. This book was released on 2011-07-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are all children exposed to the same linguistic input, and do they follow the same route in acquisition? The answer is no: The language that children hear differs even within a social class or cultural setting, as do the paths individual children take. The linguistic signal itself is also variable, both within and across speakers - the same sound is different across words; the same speech act can be realized with different constructions. The challenge here is to explain, given their diversity of experience, how children arrive at similar generalizations about their first language. This volume brings together studies of phonology, morphology, and syntax in development, to present a new perspective on how experience and variation shape children's linguistic generalizations. The papers deal with variation in forms, learning processes, and speaker features, and assess the impact of variation on the mechanisms and outcomes of language learning.
Book Synopsis Generalization With Deep Learning: For Improvement On Sensing Capability by : Zhenghua Chen
Download or read book Generalization With Deep Learning: For Improvement On Sensing Capability written by Zhenghua Chen and published by World Scientific. This book was released on 2021-04-07 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Book Synopsis Generalization of Knowledge by : Marie T. Banich
Download or read book Generalization of Knowledge written by Marie T. Banich and published by Psychology Press. This book was released on 2011-01-07 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume takes a multidisciplinary perspective on generalization of knowledge from several fields associated with Cognitive Science, including Cognitive Neuroscience, Computer Science, Education, Linguistics, Developmental Science, and Speech, Language and Hearing Sciences. The aim is to derive general principles from triangulation across different disciplines and approaches.
Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Book Synopsis Encyclopedia of Machine Learning by : Claude Sammut
Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Advanced Computing, Networking and Security by : P. Santhi Thilagam
Download or read book Advanced Computing, Networking and Security written by P. Santhi Thilagam and published by Springer. This book was released on 2012-04-02 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the International Conference on Advanced Computing, Networking and Security, ADCONS 2011, held in Surathkal, India, in December 2011. The 73 papers included in this book were carefully reviewed and selected from 289 submissions. The papers are organized in topical sections on distributed computing, image processing, pattern recognition, applied algorithms, wireless networking, sensor networks, network infrastructure, cryptography, Web security, and application security.
Download or read book Early Algebraization written by Jinfa Cai and published by Springer Science & Business Media. This book was released on 2011-02-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this volume, the authors address the development of students’ algebraic thinking in the elementary and middle school grades from curricular, cognitive, and instructional perspectives. The volume is also international in nature, thus promoting a global dialogue on the topic of early Algebraization.
Book Synopsis Concept-Based Inquiry in Action by : Carla Marschall
Download or read book Concept-Based Inquiry in Action written by Carla Marschall and published by Corwin Press. This book was released on 2018-05-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create a thinking classroom that helps students move from the factual to the conceptual Concept-Based Inquiry is a framework for inquiry that promotes deep understanding. The key is using guiding questions to help students inquire into concepts and the relationships between them. Concept-Based Inquiry in Action provides teachers with the tools and resources necessary to organize and focus student learning around concepts and conceptual relationships that support the transfer of understanding. Step by step, the authors lead both new and experienced educators to implement teaching strategies that support the realization of inquiry-based learning for understanding in any K–12 classroom.
Book Synopsis Approaches to Algebra by : N. Bednarz
Download or read book Approaches to Algebra written by N. Bednarz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Greek geometry, there is an arithmetic of magnitudes in which, in terms of numbers, only integers are involved. This theory of measure is limited to exact measure. Operations on magnitudes cannot be actually numerically calculated, except if those magnitudes are exactly measured by a certain unit. The theory of proportions does not have access to such operations. It cannot be seen as an "arithmetic" of ratios. Even if Euclidean geometry is done in a highly theoretical context, its axioms are essentially semantic. This is contrary to Mahoney's second characteristic. This cannot be said of the theory of proportions, which is less semantic. Only synthetic proofs are considered rigorous in Greek geometry. Arithmetic reasoning is also synthetic, going from the known to the unknown. Finally, analysis is an approach to geometrical problems that has some algebraic characteristics and involves a method for solving problems that is different from the arithmetical approach. 3. GEOMETRIC PROOFS OF ALGEBRAIC RULES Until the second half of the 19th century, Euclid's Elements was considered a model of a mathematical theory. This may be one reason why geometry was used by algebraists as a tool to demonstrate the accuracy of rules otherwise given as numerical algorithms. It may also be that geometry was one way to represent general reasoning without involving specific magnitudes. To go a bit deeper into this, here are three geometric proofs of algebraic rules, the frrst by Al-Khwarizmi, the other two by Cardano.
Book Synopsis Constructions at Work by : Adele E. Goldberg
Download or read book Constructions at Work written by Adele E. Goldberg and published by Oxford University Press, USA. This book was released on 2006 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Includes selected classic and contemporary papers in four areas, this text introduces each field, providing technical background for the non-specialist and explaining the underlying connections across the disciplines.
Book Synopsis The Transfer of Learning by : Sarah Leberman
Download or read book The Transfer of Learning written by Sarah Leberman and published by CRC Press. This book was released on 2016-02-17 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book addresses a crucial issue for all involved in education and training: the transfer of learning to new and different contexts. Educators, employers and learners face the problem of ensuring that what is learnt in the classroom is able to be adapted and used in the workplace. It focuses on adult learners in professional and vocational contexts. The authors provide an accessible book on the transfer of learning which draws on multi-disciplinary perspectives from education, psychology and management. The Transfer of Learning will be useful both for postgraduate students and for practitioners wanting to deepen their understanding of transfer and for those interested in practical applications. It combines theory and practice from international research and the authors' own case studies of transfer involving learners engaged in professional development and study towards qualifications. Theories of adult learning, change and lifelong learning are discussed in relation to the transfer of learning. The purpose of this book is to emphasise to tertiary educators and trainers the importance of transfer and in doing so highlight the participants' voices as central foci in coming to an understanding of the process. By doing this it balances the literature which has to date emphasized transfer from a trainer's and/or organization's perspective. There has been little if any substantive material on tertiary transfer issues and yet demands are increasing for tertiary education providers to be more accountable and more focused on developing students' ability to use their learning in everyday work situations. The book is unique in that it adopts a phenomenological perspective and underscores the significance of the participants' voices in understanding issues.
Book Synopsis A Theory of Learning and Generalization by : Mathukumalli Vidyasagar
Download or read book A Theory of Learning and Generalization written by Mathukumalli Vidyasagar and published by Springer. This book was released on 1997 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Theory of Learning and Generalization provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one "identify" the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? This is the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics. An extensive references section and open problems will help readers to develop their own work in the field.
Book Synopsis Learning with Recurrent Neural Networks by : Barbara Hammer
Download or read book Learning with Recurrent Neural Networks written by Barbara Hammer and published by Springer. This book was released on 2014-03-12 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Folding networks, a generalisation of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively.
Book Synopsis Machine Learning Proceedings 1992 by : Peter Edwards
Download or read book Machine Learning Proceedings 1992 written by Peter Edwards and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1992
