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Predicting Structured Data
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Author :Neural Information Processing Systems Foundation Publisher :MIT Press ISBN 13 :0262026171 Total Pages :361 pages Book Rating :4.2/5 (62 download)
Book Synopsis Predicting Structured Data by : Neural Information Processing Systems Foundation
Download or read book Predicting Structured Data written by Neural Information Processing Systems Foundation and published by MIT Press. This book was released on 2007 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Book Synopsis Predicting Structured Data by : Gökhan Bakir
Download or read book Predicting Structured Data written by Gökhan Bakir and published by Mit Press. This book was released on 2007-07-27 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Contributors Yasemin Altun, Gökhan Bakir, Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumé III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pérez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schölkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston.
Book Synopsis Deep Learning with Structured Data by : Mark Ryan
Download or read book Deep Learning with Structured Data written by Mark Ryan and published by Simon and Schuster. This book was released on 2020-12-08 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Book Synopsis Advanced Structured Prediction by : Sebastian Nowozin
Download or read book Advanced Structured Prediction written by Sebastian Nowozin and published by MIT Press. This book was released on 2014-12-05 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný
Book Synopsis Linguistic Structure Prediction by : Noah A. Smith
Download or read book Linguistic Structure Prediction written by Noah A. Smith and published by Springer Nature. This book was released on 2022-05-31 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Book Synopsis Structured Learning and Prediction in Computer Vision by : Sebastian Nowozin
Download or read book Structured Learning and Prediction in Computer Vision written by Sebastian Nowozin and published by Now Publishers Inc. This book was released on 2011 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
Book Synopsis Intelligent Data Engineering and Automated Learning -- IDEAL 2012 by : Hujun Yin
Download or read book Intelligent Data Engineering and Automated Learning -- IDEAL 2012 written by Hujun Yin and published by Springer. This book was released on 2012-08-01 with total page 862 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
Book Synopsis Predictive Analytics, Data Mining and Big Data by : S. Finlay
Download or read book Predictive Analytics, Data Mining and Big Data written by S. Finlay and published by Springer. This book was released on 2014-07-01 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
Book Synopsis Machine Learning Pocket Reference by : Matt Harrison
Download or read book Machine Learning Pocket Reference written by Matt Harrison and published by "O'Reilly Media, Inc.". This book was released on 2019-08-27 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Book Synopsis Big data and artificial intelligence in ophthalmology - clinical application and future exploration by : Tae-im Kim
Download or read book Big data and artificial intelligence in ophthalmology - clinical application and future exploration written by Tae-im Kim and published by Frontiers Media SA. This book was released on 2023-12-27 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Structured Prediction by : Sebastian Nowozin
Download or read book Advanced Structured Prediction written by Sebastian Nowozin and published by MIT Press. This book was released on 2014-11-21 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný
Book Synopsis Intelligent Systems and Applications by : Kohei Arai
Download or read book Intelligent Systems and Applications written by Kohei Arai and published by Springer Nature. This book was released on 2022-08-31 with total page 859 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a remarkable collection of chapters covering a wide domain of topics related to artificial intelligence and its applications to the real world. The conference attracted a total of 494 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-reviewed process. Of the total submissions, 176 submissions have been selected to be included in these proceedings. It is difficult to imagine how artificial intelligence has become an inseparable part of our life. From mobile phones, smart watches, washing machines to smart homes, smart cars, and smart industries, artificial intelligence has helped to revolutionize the whole globe. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. Distinguished researchers have made valuable studies to understand the various bottlenecks existing in different arenas and how they can be overcome with the use of intelligent systems. This book also provides new directions and dimensions of future research work. We hope that readers find the volume interesting and valuable.
Book Synopsis Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning by : Kumar, D. Satish
Download or read book Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning written by Kumar, D. Satish and published by IGI Global. This book was released on 2023-09-25 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.
Book Synopsis Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications by : Gary Miner
Download or read book Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications written by Gary Miner and published by Academic Press. This book was released on 2012-01-25 with total page 1000 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix
Download or read book Smart Health written by Hsinchun Chen and published by Springer Nature. This book was released on 2019-11-20 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed conference proceedings of the International Conference for Smart Health, ICSH 2019, held in Shenzhen, China, in July 2019. The 34 full papers and 1 short papers presented were carefully reviewed and selected from 43 submissions. In this book a lively exchange and collaborations between the growing international smart health research scholars and communities has been introduced, and to advance our understanding about the technical, practical, economic, behavioral, and social issues center on smart health . The selected papers are organized into the following topics: Precision Medicine and Telehealth, Social, Psychosocial and Behavioral Determinants of Health, Data science, Analytics, Clinical and Business Intelligence, Clinical Informatics and Clinician Engagement.
Download or read book Discovery Science written by Tapio Elomaa and published by Springer Science & Business Media. This book was released on 2011-09-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22nd International Conference on Algorithmic Learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.
Book Synopsis Deep Learning Tools for Predicting Stock Market Movements by : Renuka Sharma
Download or read book Deep Learning Tools for Predicting Stock Market Movements written by Renuka Sharma and published by John Wiley & Sons. This book was released on 2024-04-10 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
