Boosting
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Boosting
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Author : Robert E. Schapire
language : en
Publisher: MIT Press
Release Date : 2012
Boosting written by Robert E. Schapire and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Ai Powered Search
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Author : Trey Grainger
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-28
Ai Powered Search written by Trey Grainger and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.
AI-Powered Search teaches you the latest machine-learning techniques. Ideal for software developers or data scientists familiar with the basics of search engine development, it will show you ways to create content that will constantly get smarter and automatically deliver better, more relevant search experiences.
Dc Dc Converters For Future Renewable Energy Systems
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Author : Neeraj Priyadarshi
language : en
Publisher: Springer Nature
Release Date : 2021-09-27
Dc Dc Converters For Future Renewable Energy Systems written by Neeraj Priyadarshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Technology & Engineering categories.
The book presents the analysis and control of numerous DC-DC converters widely used in several applications such as standalone, grid integration, and motor drives-based renewable energy systems. The book provides extensive simulation and practical analysis of recent and advanced DC-DC power converter topologies. This self-contained book contributes to DC-DC converters design, control techniques, and industrial as well as domestic applications of renewable energy systems. This volume will be useful for undergraduate/postgraduate students, energy planners, designers, system analysis, and system governors.
Machine Learning Via Rust
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Author : Evan Pradipta Hardinatha
language : en
Publisher: RantAI
Release Date : 2024-10-14
Machine Learning Via Rust written by Evan Pradipta Hardinatha and has been published by RantAI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-14 with Computers categories.
Transform Machine Learning with Rust! 🤖🦀 Introducing MLVR - Machine Learning via Rust—the groundbreaking textbook that seamlessly blends the theoretical rigor of machine learning with the modern, high-performance capabilities of the Rust programming language! 🚀 Whether you're a student embarking on your machine learning journey or a professional looking to elevate your skills, MLVR is your comprehensive guide to mastering machine learning with Rust’s unparalleled strengths in performance, safety, and concurrency. ✨ Why Choose MLVR? 🔍 Comprehensive Coverage: From classical models like linear regression and neural networks to cutting-edge techniques such as AutoML and reinforcement learning, MLVR covers it all. 💡 Modern Integration: Leverage Rust’s unique ownership model and advanced type system to implement machine learning algorithms with unmatched safety and efficiency. 🛠️ Practical Implementation: Benefit from step-by-step coding guides, clear explanations, and real-world applications that bridge the gap between theory and practice. 🤖 Performance & Safety: Harness Rust’s core strengths to build machine learning models that are not only fast but also memory-safe and concurrent. Unlock the Benefits: ✅ High Performance: Optimize machine learning models to run at peak speed using Rust’s low-level control without compromising on safety. ✅ Scalable Solutions: Implement scalable and efficient machine learning systems that can handle large datasets and complex computations. ✅ Robust Deployments: Deploy machine learning models with confidence, knowing that Rust’s strong type system and ownership model prevent common programming errors. What You'll Explore: Foundations of Machine Learning: Understand the essential concepts and algorithms that form the backbone of machine learning. Advanced Techniques: Dive into sophisticated methods like AutoML and reinforcement learning, tailored for Rust’s ecosystem. Real-World Applications: Apply your knowledge to real-world projects, showcasing the practical power of Rust in machine learning. Optimization Strategies: Learn how to fine-tune your models for maximum performance and efficiency using Rust’s capabilities. Perfect For: Students seeking a solid foundation in machine learning with a modern programming language. Professionals aiming to enhance their machine learning expertise and optimize their Rust projects. Developers of all levels who want to implement, optimize, and deploy machine learning models effectively using Rust. Embrace the future of machine learning—transform your skills and projects with MLVR - Machine Learning via Rust’s innovative and comprehensive approach! 📚🌟 Start your journey towards mastering machine learning with Rust today and unlock new possibilities in this rapidly evolving field! 🏆 #MachineLearning #RustProgramming #MLVR #DataScience #AI #TechBooks #LearnRust #DeveloperSkills #SoftwareEngineering
Multiple Classifier Systems
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Author : Fabio Roli
language : en
Publisher: Springer
Release Date : 2003-08-02
Multiple Classifier Systems written by Fabio Roli and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-02 with Computers categories.
This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications.
Statistical Learning From A Regression Perspective
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Author : Richard A. Berk
language : en
Publisher: Springer Nature
Release Date : 2020-06-29
Statistical Learning From A Regression Perspective written by Richard A. Berk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Mathematics categories.
This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
Prediction And Analysis For Knowledge Representation And Machine Learning
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Author : Avadhesh Kumar
language : en
Publisher: CRC Press
Release Date : 2022-01-31
Prediction And Analysis For Knowledge Representation And Machine Learning written by Avadhesh Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-31 with Computers categories.
A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.
Learning Theory And Kernel Machines
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Author : Bernhard Schoelkopf
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-08-11
Learning Theory And Kernel Machines written by Bernhard Schoelkopf 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 2003-08-11 with Computers categories.
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Advances In Pattern Recognition
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Author : Francesc J. Ferri
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-08-23
Advances In Pattern Recognition written by Francesc J. Ferri 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 2000-08-23 with Computers categories.
This book constitutes the joint refereed proceedings of the 8th International Workshop on Structural and Syntactic Pattern Recognition and the 3rd International Workshop on Statistical Techniques in Pattern Recognition, SSPR 2000 and SPR 2000, held in Alicante, Spain in August/September 2000. The 52 revised full papers presented together with five invited papers and 35 posters were carefully reviewed and selected from a total of 130 submissions. The book offers topical sections on hybrid and combined methods, document image analysis, grammar and language methods, structural matching, graph-based methods, shape analysis, clustering and density estimation, object recognition, general methodology, and feature extraction and selection.
Data Mining And Knowledge Discovery Handbook
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Author : Oded Z. Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2005
Data Mining And Knowledge Discovery Handbook written by Oded Z. Maimon 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 2005 with Computers categories.
Organizes major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). This book provides algorithmic descriptions of classic methods, and also suitable for professionals in fields such as computing applications, information systems management, and more.