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Author:
Publisher: KWB Entertainment Inc.
ISBN: 0974952931
Category :
Languages : en
Pages : 189
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Book Description
Author:
Publisher: KWB Entertainment Inc.
ISBN: 0974952931
Category :
Languages : en
Pages : 189
Get Book Here
Book Description
Author: Hang Li
Publisher: Springer Nature
ISBN: 303102141X
Category : Computers
Languages : en
Pages : 107
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Book Description
Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Author: Tie-Yan Liu
Publisher: Springer Science & Business Media
ISBN: 3642142672
Category : Computers
Languages : en
Pages : 282
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Book Description
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.
Author: Hang Li
Publisher: Springer Nature
ISBN: 303102155X
Category : Computers
Languages : en
Pages : 107
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Book Description
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Author: Stephanie Kremer
Publisher: GRIN Verlag
ISBN: 3668829217
Category : Business & Economics
Languages : en
Pages : 12
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Book Description
Document from the year 2018 in the subject Business economics - Business Management, Corporate Governance, grade: 1,3, Munich University of Applied Sciences, language: English, abstract: Only in Germany, 23.9 million of the over 14-year-old german-speaking population used search engines on the internet for their information search daily in 2017. Compared to 21.08 milion in 2016, this is a growth of 13.38 percent. According to other studies, over 85 percent of all internet sessions start with the type-in into search engines. Therefore, Search Engine Optimization (SEO) offers a huge potential for companies to deliver the right information to interested users when they ask for it actively. To succeed against their competitors, it is important for companies to appear as high as possible in the search engine results pages (SERPs). The question about the most important factors to consider in SEO for good ranking positions is always highly discussed in SEO communities because Google reveals as little as possible to avoid manipulation. But one thing appears to be clear: because of highly developed machine-learning-algorithms which influence Googles’ valuation of a website for its ranking, classical ranking factors can no longer be used as standard for every search query anymore, but relevant content as well as user experience have become important.
Author: United States. Central Intelligence Agency
Publisher: Potomac Books
ISBN: 9781574886412
Category : Political Science
Languages : en
Pages : 712
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Book Description
By intelligence officials for intelligent people
Author: Minho Lee
Publisher: Springer
ISBN: 3642420427
Category : Computers
Languages : en
Pages : 794
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Book Description
The three volume set LNCS 8226, LNCS 8227 and LNCS 8228 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2013, held in Daegu, Korea, in November 2013. The 180 full and 75 poster papers presented together with 4 extended abstracts were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The specific topics covered are as follows: cognitive science and artificial intelligence; learning theory, algorithms and architectures; computational neuroscience and brain imaging; vision, speech and signal processing; control, robotics and hardware technologies and novel approaches and applications.
Author: Amy N. Langville
Publisher: Princeton University Press
ISBN: 069116231X
Category : Computers
Languages : en
Pages : 265
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Book Description
The mathematics behind today's most widely used rating and ranking methods A website's ranking on Google can spell the difference between success and failure for a new business. NCAA football ratings determine which schools get to play for the big money in postseason bowl games. Product ratings influence everything from the clothes we wear to the movies we select on Netflix. Ratings and rankings are everywhere, but how exactly do they work? Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses. Amy Langville and Carl Meyer provide the first comprehensive overview of the mathematical algorithms and methods used to rate and rank sports teams, political candidates, products, Web pages, and more. In a series of interesting asides, Langville and Meyer provide fascinating insights into the ingenious contributions of many of the field's pioneers. They survey and compare the different methods employed today, showing why their strengths and weaknesses depend on the underlying goal, and explaining why and when a given method should be considered. Langville and Meyer also describe what can and can't be expected from the most widely used systems. The science of rating and ranking touches virtually every facet of our lives, and now you don't need to be an expert to understand how it really works. Who's #1? is the definitive introduction to the subject. It features easy-to-understand examples and interesting trivia and historical facts, and much of the required mathematics is included.
Author: José Cordeiro
Publisher: Springer
ISBN: 3642366082
Category : Computers
Languages : en
Pages : 388
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Book Description
This book contains the thoroughly refereed and revised best papers from the 8th International Conference on Web Information Systems and Technologies, WEBIST 2012, held in Porto, Portugal, in April 2012, and organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), in collaboration with ACM SIGMIS. The 23 papers presented in this book were carefully reviewed and selected from 184 submissions. The papers were selected from those with the best reviews also taking into account the quality of their presentation at the conference. The papers are grouped into parts on Internet Technology; Web Interfaces and Applications; Society, e-Business, and e-Government; Web Intelligence; and Mobile Information Systems.
Author: Anne H.H. Ngu
Publisher: Springer Science & Business Media
ISBN: 3540300171
Category : Business & Economics
Languages : en
Pages : 790
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Book Description
This book constitutes the proceedings of the 6th International Conference on Web Information Systems Engineering, WISE 2005, held in New York, NY, USA, in November 2005. The 30 revised full papers and 20 revised short papers presented together with 18 poster papers were carefully reviewed and selected from 259 submissions. The papers are organized in topical sections on Web mining, Web information retrieval, metadata management, ontology and semantic Web, XML, Web service method, Web service structure, collaborative methodology, P2P, ubiquitous and mobile, document retrieval applications, Web services and e-commerce, recommendation and Web information extraction, P2P, grid and distributed management, and advanced issues. The presentation is rounded off by 14 industrial papers and the abstracts of 4 tutorial sessions.
