Large Scale Kernel Machines

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Large-scale Kernel Machines

Author : Léon Bottou
Publisher : MIT Press
ISBN 13 : 0262026252
Total Pages : 409 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Large-scale Kernel Machines by : Léon Bottou

Download or read book Large-scale Kernel Machines written by Léon Bottou and published by MIT Press. This book was released on 2007 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically. Contributors Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov

Regularization, Optimization, Kernels, and Support Vector Machines

Author : Johan A.K. Suykens
Publisher : CRC Press
ISBN 13 : 1482241390
Total Pages : 528 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Regularization, Optimization, Kernels, and Support Vector Machines by : Johan A.K. Suykens

Download or read book Regularization, Optimization, Kernels, and Support Vector Machines written by Johan A.K. Suykens and published by CRC Press. This book was released on 2014-10-23 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Computational Science – ICCS 2020

Author : Valeria V. Krzhizhanovskaya
Publisher : Springer Nature
ISBN 13 : 3030504174
Total Pages : 715 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Computational Science – ICCS 2020 by : Valeria V. Krzhizhanovskaya

Download or read book Computational Science – ICCS 2020 written by Valeria V. Krzhizhanovskaya and published by Springer Nature. This book was released on 2020-06-18 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

Large Scale Machine Learning with Python

Author : Bastiaan Sjardin
Publisher : Packt Publishing Ltd
ISBN 13 : 1785888021
Total Pages : 420 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Large Scale Machine Learning with Python by : Bastiaan Sjardin

Download or read book Large Scale Machine Learning with Python written by Bastiaan Sjardin and published by Packt Publishing Ltd. This book was released on 2016-08-03 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Foundations of Large-Scale Multimedia Information Management and Retrieval

Author : Edward Y. Chang
Publisher : Springer Science & Business Media
ISBN 13 : 9783642204296
Total Pages : 291 pages
Book Rating : 4.2/5 (42 download)

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Book Synopsis Foundations of Large-Scale Multimedia Information Management and Retrieval by : Edward Y. Chang

Download or read book Foundations of Large-Scale Multimedia Information Management and Retrieval written by Edward Y. Chang and published by Springer Science & Business Media. This book was released on 2011-08-27 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions. The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval. Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.

Machine Learning, Optimization, and Data Science

Author : Giuseppe Nicosia
Publisher : Springer
ISBN 13 : 3030137090
Total Pages : 584 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Machine Learning, Optimization, and Data Science by : Giuseppe Nicosia

Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer. This book was released on 2019-02-16 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Kernel Methods for Machine Learning with Math and R

Author : Joe Suzuki
Publisher : Springer Nature
ISBN 13 : 9811903980
Total Pages : 203 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Kernel Methods for Machine Learning with Math and R by : Joe Suzuki

Download or read book Kernel Methods for Machine Learning with Math and R written by Joe Suzuki and published by Springer Nature. This book was released on 2022-05-04 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book’s main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

Kernel Methods for Machine Learning with Math and Python

Author : Joe Suzuki
Publisher : Springer Nature
ISBN 13 : 9811904014
Total Pages : 216 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Kernel Methods for Machine Learning with Math and Python by : Joe Suzuki

Download or read book Kernel Methods for Machine Learning with Math and Python written by Joe Suzuki and published by Springer Nature. This book was released on 2022-05-14 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

Machine Learning and Knowledge Discovery in Databases

Author : Walter Daelemans
Publisher : Springer
ISBN 13 : 354087481X
Total Pages : 698 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Walter Daelemans

Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer. This book was released on 2008-08-17 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Machine Learning and Data Mining in Pattern Recognition

Author : Petra Perner
Publisher : Springer Science & Business Media
ISBN 13 : 364203070X
Total Pages : 837 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Machine Learning and Data Mining in Pattern Recognition by : Petra Perner

Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 837 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Mathematical Models for Remote Sensing Image Processing

Author : Gabriele Moser
Publisher : Springer
ISBN 13 : 3319663305
Total Pages : 441 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Mathematical Models for Remote Sensing Image Processing by : Gabriele Moser

Download or read book Mathematical Models for Remote Sensing Image Processing written by Gabriele Moser and published by Springer. This book was released on 2017-11-28 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book maximizes reader insights into the field of mathematical models and methods for the processing of two-dimensional remote sensing images. It presents a broad analysis of the field, encompassing passive and active sensors, hyperspectral images, synthetic aperture radar (SAR), interferometric SAR, and polarimetric SAR data. At the same time, it addresses highly topical subjects involving remote sensing data types (e.g., very high-resolution images, multiangular or multiresolution data, and satellite image time series) and analysis methodologies (e.g., probabilistic graphical models, hierarchical image representations, kernel machines, data fusion, and compressive sensing) that currently have primary importance in the field of mathematical modelling for remote sensing and image processing. Each chapter focuses on a particular type of remote sensing data and/or on a specific methodological area, presenting both a thorough analysis of the previous literature and a methodological and experimental discussion of at least two advanced mathematical methods for information extraction from remote sensing data. This organization ensures that both tutorial information and advanced subjects are covered. With each chapter being written by research scientists from (at least) two different institutions, it offers multiple professional experiences and perspectives on each subject. The book also provides expert analysis and commentary from leading remote sensing and image processing researchers, many of whom serve on the editorial boards of prestigious international journals in these fields, and are actively involved in international scientific societies. Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of mathematical models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners.

Artificial Intelligence and Machine Learning

Author : Toon Calders
Publisher : Springer Nature
ISBN 13 : 3031391446
Total Pages : 190 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Artificial Intelligence and Machine Learning by : Toon Calders

Download or read book Artificial Intelligence and Machine Learning written by Toon Calders and published by Springer Nature. This book was released on 2023-09-04 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of the best papers of the 34th Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2022, held in Mechelen, Belgium, in November 2022. The 11 papers presented in this volume were carefully reviewed and selected from 134 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.

Machine Learning and Knowledge Discovery in Databases

Author : Peter A. Flach
Publisher : Springer
ISBN 13 : 3642334865
Total Pages : 867 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Peter A. Flach

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peter A. Flach and published by Springer. This book was released on 2012-09-11 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition

Author :
Publisher : ScholarlyEditions
ISBN 13 : 1464964718
Total Pages : 1136 pages
Book Rating : 4.4/5 (649 download)

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Book Synopsis Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition by :

Download or read book Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition written by and published by ScholarlyEditions. This book was released on 2012-01-09 with total page 1136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Artificial Intelligence, Robotics and Machine Learning. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence, Robotics and Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Scaling Up Machine Learning

Author : Ron Bekkerman
Publisher : Cambridge University Press
ISBN 13 : 0521192242
Total Pages : 493 pages
Book Rating : 4.5/5 (211 download)

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Book Synopsis Scaling Up Machine Learning by : Ron Bekkerman

Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Pattern Recognition

Author : Michael Goesele
Publisher : Springer
ISBN 13 : 3642159869
Total Pages : 574 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Pattern Recognition by : Michael Goesele

Download or read book Pattern Recognition written by Michael Goesele and published by Springer. This book was released on 2010-09-17 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: On behalf of the organizing committee, we would like to welcome you to Da- nd stadt and DAGM 2010,the 32 Annual Symposium of the German Association for Pattern Recognition. The technical program covered all aspects of pattern recognition and, to name only a few areas, ranged from 3D reconstruction, to object recognition and medical applications. The result is re?ected in these proceedings, which contain the papers presented at DAGM 2010. Our call for papers resulted in 134 submissions from institutions in 21 countries. Each paper underwent a rigorous reviewing process and was assigned to at least three program committee m- bers for review. The reviewing phase was followed by a discussion phase among the respective program committee members in order to suggest papers for - ceptance. The ?nal decision was taken during a program committee meeting held in Darmstadt based on all reviews, the discussion results and, if necessary, additional reviewing. Based on this rigorous process we selected a total of 57 papers, corresponding to an acceptance rate of below 45%. Out of all accepted papers, 24 were chosen for oral and 33 for poster presentation. All accepted - pers have been published in these proceedings and given the same number of pages. We would like to thank all members of the program committee as well as the external reviewers for their valuable and highly appreciated contribution to the community.

Textual Information Access

Author : Eric Gaussier
Publisher : John Wiley & Sons
ISBN 13 : 1118562801
Total Pages : 334 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Textual Information Access by : Eric Gaussier

Download or read book Textual Information Access written by Eric Gaussier and published by John Wiley & Sons. This book was released on 2013-02-04 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access: - information extraction and retrieval; - text classification and clustering; - opinion mining; - comprehension aids (automatic summarization, machine translation, visualization). In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications concerned, by highlighting the relationship between models and applications and by illustrating the behavior of each model on real collections. Textual Information Access is organized around four themes: informational retrieval and ranking models, classification and clustering (regression logistics, kernel methods, Markov fields, etc.), multilingualism and machine translation, and emerging applications such as information exploration. Contents Part 1: Information Retrieval 1. Probabilistic Models for Information Retrieval, Stéphane Clinchant and Eric Gaussier. 2. Learnable Ranking Models for Automatic Text Summarization and Information Retrieval, Massih-Réza Amini, David Buffoni, Patrick Gallinari, Tuong Vinh Truong and Nicolas Usunier. Part 2: Classification and Clustering 3. Logistic Regression and Text Classification, Sujeevan Aseervatham, Eric Gaussier, Anestis Antoniadis, Michel Burlet and Yves Denneulin. 4. Kernel Methods for Textual Information Access, Jean-Michel Renders. 5. Topic-Based Generative Models for Text Information Access, Jean-Cédric Chappelier. 6. Conditional Random Fields for Information Extraction, Isabelle Tellier and Marc Tommasi. Part 3: Multilingualism 7. Statistical Methods for Machine Translation, Alexandre Allauzen and François Yvon. Part 4: Emerging Applications 8. Information Mining: Methods and Interfaces for Accessing Complex Information, Josiane Mothe, Kurt Englmeier and Fionn Murtagh. 9. Opinion Detection as a Topic Classification Problem, Juan-Manuel Torres-Moreno, Marc El-Bèze, Patrice Bellot and Fréderic Béchet.