Efficient Learning Machines


Efficient Learning Machines
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Efficient Learning Machines


Efficient Learning Machines
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Author : Mariette Awad
language : en
Publisher: Apress
Release Date : 2015-04-27

Efficient Learning Machines written by Mariette Awad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.


Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.



Efficient Learning Machines


Efficient Learning Machines
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Author : Mariette Awad
language : en
Publisher:
Release Date : 2015

Efficient Learning Machines written by Mariette Awad and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.




Efficient Learning Machines


Efficient Learning Machines
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Author : Maximilian Alber
language : en
Publisher:
Release Date : 2019

Efficient Learning Machines written by Maximilian Alber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.




Extreme Learning Machines 2013 Algorithms And Applications


Extreme Learning Machines 2013 Algorithms And Applications
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Author : Fuchen Sun
language : en
Publisher: Springer
Release Date : 2014-07-08

Extreme Learning Machines 2013 Algorithms And Applications written by Fuchen Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-08 with Technology & Engineering categories.


In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability. This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of “learning without iterative tuning". This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.



An Introduction To Computational Learning Theory


An Introduction To Computational Learning Theory
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Author : Michael J. Kearns
language : en
Publisher: MIT Press
Release Date : 1994-08-15

An Introduction To Computational Learning Theory written by Michael J. Kearns and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-08-15 with Computers categories.


Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.



Advances In Independent Component Analysis And Learning Machines


Advances In Independent Component Analysis And Learning Machines
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Author : Ella Bingham
language : en
Publisher: Academic Press
Release Date : 2015-05-14

Advances In Independent Component Analysis And Learning Machines written by Ella Bingham and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-14 with Technology & Engineering categories.


In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep learning Machine vision and image retrieval A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning. A diverse set of application fields, ranging from machine vision to science policy data. Contributions from leading researchers in the field.



An Introduction To Support Vector Machines And Other Kernel Based Learning Methods


An Introduction To Support Vector Machines And Other Kernel Based Learning Methods
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Author : Nello Cristianini
language : en
Publisher: Cambridge University Press
Release Date : 2000-03-23

An Introduction To Support Vector Machines And Other Kernel Based Learning Methods written by Nello Cristianini and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-03-23 with Computers categories.


This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.



Artificial Neural Networks And Machine Learning Icann 2011


Artificial Neural Networks And Machine Learning Icann 2011
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Author : Timo Honkela
language : en
Publisher: Springer
Release Date : 2011-06-13

Artificial Neural Networks And Machine Learning Icann 2011 written by Timo Honkela and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-13 with Computers categories.


This two volume set LNCS 6791 and LNCS 6792 constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.



Artificial Intelligence For Asset Management And Investment


Artificial Intelligence For Asset Management And Investment
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Author : Al Naqvi
language : en
Publisher: John Wiley & Sons
Release Date : 2021-02-09

Artificial Intelligence For Asset Management And Investment written by Al Naqvi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Business & Economics categories.


Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.



Learning To Classify Text Using Support Vector Machines


Learning To Classify Text Using Support Vector Machines
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Author : Thorsten Joachims
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Learning To Classify Text Using Support Vector Machines written by Thorsten Joachims 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.


Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.