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Reverse Hypothesis Machine Learning
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Book Synopsis Reverse Hypothesis Machine Learning by : Parag Kulkarni
Download or read book Reverse Hypothesis Machine Learning written by Parag Kulkarni and published by Springer. This book was released on 2017-03-30 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
Book Synopsis Choice Computing: Machine Learning and Systemic Economics for Choosing by : Parag Kulkarni
Download or read book Choice Computing: Machine Learning and Systemic Economics for Choosing written by Parag Kulkarni and published by Springer Nature. This book was released on 2022-08-28 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products – help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.
Book Synopsis AI, Consciousness and The New Humanism by : Sangeetha Menon
Download or read book AI, Consciousness and The New Humanism written by Sangeetha Menon and published by Springer Nature. This book was released on with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Explainable, Interpretable, and Transparent AI Systems by : B. K. Tripathy
Download or read book Explainable, Interpretable, and Transparent AI Systems written by B. K. Tripathy and published by CRC Press. This book was released on 2024-08-23 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.
Book Synopsis Proceedings of the 2nd International Conference on Data Engineering and Communication Technology by : Anand J. Kulkarni
Download or read book Proceedings of the 2nd International Conference on Data Engineering and Communication Technology written by Anand J. Kulkarni and published by Springer. This book was released on 2018-10-03 with total page 695 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research work presented at the 2nd International Conference on Data Engineering and Communication Technology (ICDECT) held on December 15–16, 2017 at Symbiosis International University, Pune, Maharashtra, India. It discusses advanced, multi-disciplinary research into smart computing, information systems and electronic systems, focusing on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide feasible solutions to varied problems in society, the environment and industry. It also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in a variety of disciplines of computer science and electronics engineering.
Book Synopsis Forward and Reverse Cross Validation for Comparing Machine Learning Methods by : Weichen Wu
Download or read book Forward and Reverse Cross Validation for Comparing Machine Learning Methods written by Weichen Wu and published by . This book was released on 2006 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adversarial Machine Learning by : Yevgeniy Tu
Download or read book Adversarial Machine Learning written by Yevgeniy Tu and published by Springer Nature. This book was released on 2022-05-31 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
Book Synopsis Machine Learning in Translation by : Peng Wang
Download or read book Machine Learning in Translation written by Peng Wang and published by Taylor & Francis. This book was released on 2023-04-12 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.
Book Synopsis Adversarial Machine Learning by : Anthony D. Joseph
Download or read book Adversarial Machine Learning written by Anthony D. Joseph and published by Cambridge University Press. This book was released on 2019-02-21 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
Book Synopsis Artificial Intelligence and Machine Learning by : Bart Bogaerts
Download or read book Artificial Intelligence and Machine Learning written by Bart Bogaerts and published by Springer Nature. This book was released on 2021-01-04 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of the best papers of the 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019, held in Brussels, Belgium in November 2019. The 11 papers presented in this volume were carefully reviewed and selected from 50 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.
Book Synopsis Machine Learning as a Tool for Hypothesis Generation by : Jens Ludwig
Download or read book Machine Learning as a Tool for Hypothesis Generation written by Jens Ludwig and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about who to jail. We begin with a striking fact: The defendant's face alone matters greatly for the judge's jailing decision. In fact, an algorithm given only the pixels in the defendant's mugshot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: They are not explained by demographics (e.g. race) or existing psychology research; nor are they already known (even if tacitly) to people or even experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional dataset (e.g. cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our paper is that hypothesis generation is in and of itself a valuable activity, and hope this encourages future work in this largely "pre-scientific" stage of science.
Book Synopsis Statistical Methods for Machine Learning by : Jason Brownlee
Download or read book Statistical Methods for Machine Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-05-30 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.
Book Synopsis Human + Machine, Updated and Expanded by : Paul R. Daugherty
Download or read book Human + Machine, Updated and Expanded written by Paul R. Daugherty and published by Harvard Business Press. This book was released on 2024-09-10 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI—including generative AI—is radically transforming business. Are you ready? Accenture technology leaders Paul Daugherty and Jim Wilson provide crucial insights and advice to help you meet the challenge. Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now—in software that senses what we need, supply chains that "think" in real time, and now generative AI that is radically reshaping work and productivity. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In this updated and expanded edition of Human + Machine—including a new chapter on gen AI—Accenture technology leaders Paul Daugherty and Jim Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization, whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly—or completely reimagine them. Based on the authors' experience and research with fifteen hundred organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability and what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader's guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in the new age of AI.
Book Synopsis Machine Learning and Data Mining by : Igor Kononenko
Download or read book Machine Learning and Data Mining written by Igor Kononenko and published by Horwood Publishing. This book was released on 2007-04-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.
Book Synopsis Machine Learning: ECML-93 by : Pavel B. Brazdil
Download or read book Machine Learning: ECML-93 written by Pavel B. Brazdil and published by Springer Science & Business Media. This book was released on 1993-03-23 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops.
Book Synopsis Reverse Vaccinology by : Jayashankar Das
Download or read book Reverse Vaccinology written by Jayashankar Das and published by Elsevier. This book was released on 2024-07-26 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reverse Vaccinology: Concept, Methods and Advancement presents the development strategy of new vaccines through genome sequencing bioinformatics analysis. Reverse vaccinology promises to revolutionize vaccine development, especially for pathogens to which the classical applications of Pasteur’s principles have failed, and it is explained in detail in this book.The book is split into three sections: the first, Concept, brings the basis of reverse vaccinology, vaccine antigen discovery, and subunit vaccine; the second, Tools and Methods, describes immunoinformatic, proteomics for epitope-vaccine design, data bases, network analysis, machine learning, and NGS driven antigen screening technology; and the last one, Disease Case Study, discusses real-world examples in the development of new vaccines for diverse diseases.It is a valuable resource for bioinformaticians, researchers, students, and member of the biomedical and medical fields who want to learn more about a new and agile process for the development of new vaccines. ? Explains the fundamentals of reverse vaccinology and how it can save time in the development of new vaccines ? Focuses on the efforts to develop a vaccine candidate against various pathogens using computational approaches ? Presents databases and web servers for conducting reverse vaccinology ? Describes the screening process of potential vaccine candidate through machine learning
Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri
Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
