Download or read book Web and Big Data written by Bohan Li and published by Springer Nature. This book was released on 2023-02-09 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set, LNCS 13421, 13422 and 13423, constitutes the thoroughly refereed proceedings of the 6th International Joint Conference, APWeb-WAIM 2022, held in Nanjing, China, in August 2022. The 75 full papers presented together with 45 short papers, and 5 demonstration papers were carefully reviewed and selected from 297 submissions. The papers are organized around the following topics: Big Data Analytic and Management, Advanced database and web applications, Cloud Computing and Crowdsourcing, Data Mining, Graph Data and Social Networks, Information Extraction and Retrieval, Knowledge Graph, Machine Learning, Query processing and optimization, Recommender Systems, Security, privacy, and trust and Blockchain data management and applications, and Spatial and multi-media data.

Download or read book Information Retrieval written by Pavel Braslavski and published by Springer. This book was released on 2015-12-09 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 8th Russian Summer School on Information Retrieval, RuSSIR 2014, held in Nizhniy Novgorod, Russia, in August 2014. The volume includes 6 tutorial papers, summarizing lectures given at the event, and 8 revised papers from the school participants.The papers focus on various aspects of information retrieval.

Download or read book Advances in Information Retrieval written by Leif Azzopardi and published by Springer. This book was released on 2019-04-06 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 11437 and 11438 constitutes the refereed proceedings of the 41st European Conference on IR Research, ECIR 2019, held in Cologne, Germany, in April 2019. The 48 full papers presented together with 2 keynote papers, 44 short papers, 8 demonstration papers, 8 invited CLEF papers, 11 doctoral consortium papers, 4 workshop papers, and 4 tutorials were carefully reviewed and selected from 365 submissions. They were organized in topical sections named: Modeling Relations; Classification and Search; Recommender Systems; Graphs; Query Analytics; Representation; Reproducibility (Systems); Reproducibility (Application); Neural IR; Cross Lingual IR; QA and Conversational Search; Topic Modeling; Metrics; Image IR; Short Papers; Demonstration Papers; CLEF Organizers Lab Track; Doctoral Consortium Papers; Workshops; and Tutorials.

Download or read book Online Evaluation for Information Retrieval written by Katja Hofmann and published by . This book was released on 2016-06-07 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive overview of the topic. It shows how online evaluation is used for controlled experiments, segmenting them into experiment designs that allow absolute or relative quality assessments. It also includes an extensive discussion of recent work on data re-use, and experiment estimation based on historical data.

Download or read book Empirical Inference written by Bernhard Schölkopf and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.

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 Introduction to Derivative Free Optimization written by Andrew R. Conn and published by SIAM. This book was released on 2009-04-16 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics.

Download or read book Buyology written by Martin Lindstrom and published by Currency. This book was released on 2010-02-02 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: NEW YORK TIMES BESTSELLER • “A fascinating look at how consumers perceive logos, ads, commercials, brands, and products.”—Time How much do we know about why we buy? What truly influences our decisions in today’s message-cluttered world? In Buyology, Martin Lindstrom presents the astonishing findings from his groundbreaking three-year, seven-million-dollar neuromarketing study—a cutting-edge experiment that peered inside the brains of 2,000 volunteers from all around the world as they encountered various ads, logos, commercials, brands, and products. His startling results shatter much of what we have long believed about what captures our interest—and drives us to buy. Among the questions he explores: • Does sex actually sell? • Does subliminal advertising still surround us? • Can “cool” brands trigger our mating instincts? • Can our other senses—smell, touch, and sound—be aroused when we see a product? Buyology is a fascinating and shocking journey into the mind of today's consumer that will captivate anyone who's been seduced—or turned off—by marketers' relentless attempts to win our loyalty, our money, and our minds.

Download or read book Advances in Information Retrieval written by Djoerd Hiemstra and published by Springer Nature. This book was released on 2021-03-26 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more.

Download or read book Proceedings of the International Conference on Computing and Communication Systems written by Arnab Kumar Maji and published by Springer Nature. This book was released on 2021-04-11 with total page 731 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the latest research work presented at the International Conference on Computing and Communication Systems (I3CS 2020) held at North-Eastern Hill University (NEHU), Shillong, India. The book presents original research results, new ideas and practical development experiences which concentrate on both theory and practices. It includes papers from all areas of information technology, computer science, electronics and communication engineering written by researchers, scientists, engineers and scholar students and experts from India and abroad.

Download or read book Click Models for Web Search written by Aleksandr Chuklin and published by Morgan & Claypool Publishers. This book was released on 2015-07-01 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.

Download or read book Learning to Rank for Information Retrieval written by Tie-Yan Liu and published by Springer Science & Business Media. This book was released on 2011-04-29 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Invisible Romans written by Robert C. Knapp and published by Profile Books(GB). This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Knapp brings invisible inhabitants of Rome and its vast empire to life. He seeks out the ordinary men, housewives, prostitutes, freedmen, slaves, soldiers, and gladiators, who formed the fabric of everyday life in the ancient Roman world, and the outlaws and pirates who lay beyond it. He finds their own words preserved in literature, letters, inscriptions and graffiti and their traces in the nooks and crannies of the histories, treatises, plays and poetry created by members of the elite. He tracks down and pieces together these and other tell-tale bits of evidence cast off by the visible mass of Roman history and culture, and in doing so recreates a world lost from view for two millennia. We see how everyday Romans sought to survive and thrive under the afflictions of disease, war, and violence, and to control their fates before powers that variously oppressed and ignored them. Chapters on each of the main groups reveal how their worlds were linked in need, dependence, exploitation, hope and fear. Slaves and ex-soldiers merge into the world of the outlaw; slaves become freedmen; the sons of freedmen enlist as soldiers; and the concerns of women transcend every boundary. We see them all at last in the tumult of a great empire that shaped their worlds as it reshaped the wider world around them.

Download or read book Battletech Field Manual Sldf written by Catalyst Game Labs and published by Catalyst Game Labs. This book was released on 2012-09-12 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Understanding Narrative written by James Phelan and published by . This book was released on 1994 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering essays that consider familiar and unfamiliar narratives from Bronte's Shirley to Myra Page's Moscow Yankee, from Mozart's Prague Symphony to Mungo Park's Travels in the Interior of Africa, Understanding Narrative exemplifies the range of work that this series seeks to promote. Students and scholars of British and American literature, film, and critical theory will find this volume a welcome addition to the series.

Download or read book Anagram Solver written by Bloomsbury Publishing and published by Bloomsbury Publishing. This book was released on 2009-01-01 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anagram Solver is the essential guide to cracking all types of quiz and crossword featuring anagrams. Containing over 200,000 words and phrases, Anagram Solver includes plural noun forms, palindromes, idioms, first names and all parts of speech. Anagrams are grouped by the number of letters they contain with the letters set out in alphabetical order so that once the letters of an anagram are arranged alphabetically, finding the solution is as easy as locating the word in a dictionary.

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.