Download or read book Learning Structured Prediction Models written by Ben Taskar and published by . This book was released on 2004 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
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
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
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
Download or read book Explanation Based Learning written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-30 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Explanation Based Learning A type of machine learning known as explanation-based learning, or EBL for short, takes advantage of an extremely robust, or even flawless, domain theory in order to generalize from training data or construct concepts. It also has a connection with encoding, or memory, which assists with learning. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Explanation-based learning Chapter 2: Computational linguistics Chapter 3: Natural language processing Chapter 4: Corpus linguistics Chapter 5: Parsing Chapter 6: Question answering Chapter 7: Link grammar Chapter 8: Grammar induction Chapter 9: Structured prediction Chapter 10: Deep linguistic processing (II) Answering the public top questions about explanation based learning. (III) Real world examples for the usage of explanation based learning in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of explanation based learning' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of explanation based learning.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Inductive Logic Programming written by Nicolas Lachiche and published by Springer. This book was released on 2018-03-19 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
Download or read book Rule Extraction from Support Vector Machines written by Joachim Diederich and published by Springer Science & Business Media. This book was released on 2008-01-04 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
Download or read book Computational Methods for Integrating Vision and Language written by Kenichi Kanatani and published by Springer Nature. This book was released on 2022-05-31 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones for simple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images.
Download or read book Classification as a Tool for Research written by Hermann Locarek-Junge and published by Springer Science & Business Media. This book was released on 2010-08-03 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering and Classification, Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of statistics, mathematics, computer science and artificial intelligence. They cover general methods and techniques that can be applied to a vast set of applications such as in business and economics, marketing and finance, engineering, linguistics, archaeology, musicology, biology and medical science. This volume contains the revised versions of selected papers presented during the 11th Biennial IFCS Conference and 33rd Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was organized in cooperation with the International Federation of Classification Societies (IFCS), and was hosted by Dresden University of Technology, Germany, in March 2009.
Download or read book Machine Learning and Knowledge Discovery in Databases written by Annalisa Appice and published by Springer. This book was released on 2015-08-28 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Download or read book Biomedical Engineering Systems and Technologies written by Ana Fred and published by Springer. This book was released on 2011-03-02 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Third International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2010, held in Valencia, Spain, in January 2010. The 30 revised full papers presented together with 1 invited lecture were carefully reviewed and selected from a total of 410 submissions in two rounds of reviewing and improvement. The papers cover a wide range of topics and are organized in four general topical sections on healthinf, biodevices, biosignals, and bioinformatics.
Download or read book Preference Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2010-11-19 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
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 882 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.
Download or read book Computer Vision ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 913 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Download or read book HCI in Business Government and Organizations eCommerce and Innovation written by Fiona Fui-Hoon Nah and published by Springer. This book was released on 2016-07-04 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Third International Conference on HCI in Business, Government and Organizations, HCIBGO 2016, held as part of the 18th International Conference on Human-Computer Interaction, HCII 2016, which took place in Toronto, Canada, in July 2016. HCII 2016 received a total of 4354 submissions, of which 1287 papers were accepted for publication after a careful reviewing process. The 53 papers presented in this volume are organized in topical sections named: social media for business; electronic, mobile and ubiquitous commerce; business analytics and visualization; branding, marketing and consumer behavior; and digital innovation.
