Search

This book constitutes the revised selected papers of the 6th Pacific Causal Inference Conference, PCIC 2024, held in Shanghai, China, during July 5–6, 2024. The 8 papers included in these proceedings were carefully reviewed and selected from 15 submissions. They aim to promote research and developmental activities in the fields of Causal Inference and Artificial Intelligence.

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

This book constitutes the refereed proceedings of the First CCF International Conference on Artificial Intelligence, CCF-ICAI 2018, held in Jinan, China in August, 2018. The 17 papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in topical sections on unsupervised learning, graph-based and semi-supervised learning, neural networks and deep learning, planning and optimization, AI applications.

This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015.The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations.

This book constitutes the refereed proceedings at PAKDD Workshops 2015, held in conjunction with PAKDD, the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Ho Chi Minh City, Vietnam, in May 2015. The 23 revised papers presented were carefully reviewed and selected from 57 submissions. The workshops affiliated with PAKDD 2015 include: Pattern Mining and Application of Big Data (BigPMA), Quality Issues, Measures of Interestingness and Evaluation of data mining models (QIMIE), Data Analytics for Evidence-based Healthcare (DAEBH), Vietnamese Language and Speech Processing (VLSP).

The two-volume set LNCS 9242 + 9243 constitutes the proceedings of the 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, held in Suzhou, China, in June 2015. The total of 126 papers presented in the proceedings was carefully reviewed and selected from 416 submissions. They deal with big data, neural networks, image processing, computer vision, pattern recognition and graphics, object detection, dimensionality reduction and manifold learning, unsupervised learning and clustering, anomaly detection, semi-supervised learning.

The two-volume set LNCS 9242 + 9243 constitutes the proceedings of the 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, held in Suzhou, China, in June 2015. The total of 126 papers presented in the proceedings was carefully reviewed and selected from 416 submissions. They deal with big data, neural networks, image processing, computer vision, pattern recognition and graphics, object detection, dimensionality reduction and manifold learning, unsupervised learning and clustering, anomaly detection, semi-supervised learning.

This book constitutes the refereed proceedings at PAKDD Workshops 2014, held in conjunction with the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) held in Tainan, Taiwan, in May 2014.The 73 revised papers presented were carefully reviewed and selected from 179 submissions. The workshops affiliated with PAKDD 2014 include: Data Analytics for Targeted Healthcare, DANTH; Data Mining and Decision Analytics for Public Health and Wellness, DMDA-Health; Biologically Inspired Data Mining Techniques, BDM; Mobile Data Management, Mining, and Computing on Social Networks, MobiSocial; Big Data Science and Engineering on E-Commerce, BigEC; Cloud Service Discovery, CloudSD; Mobile Sensing, Mining and Visualization for Human Behavior Inferences, MSMV-HBI; Scalable Dats Analytics: Theory and Algorithms, SDA; Algorithms for Large-Scale Information Processing in Knowledge Discovery, ALSIP; Data Mining in Social Networks, SocNet; Data Mining in Biomedical Informatics and Healthcare, DMBIH; and Pattern Mining and Application of Big Data, BigPMA.

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. The 40 full papers and the 60 short papers presented within these proceedings were carefully reviewed and selected from 371 submissions. They cover the general fields of pattern mining; social network and social media; classification; graph and network mining; applications; privacy preserving; recommendation; feature selection and reduction; machine learning; temporal and spatial data; novel algorithms; clustering; biomedical data mining; stream mining; outlier and anomaly detection; multi-sources mining; and unstructured data and text mining.

This book constitutes the thoroughly refereed revised selected papers from the Second IAPR International Workshop, PSL 2013, held in Nanjing, China, in May 2013. The 10 papers included in this volume were carefully reviewed and selected from 26 submissions. Partially supervised learning is a rapidly evolving area of machine learning. It generalizes many kinds of learning paradigms including supervised and unsupervised learning, semi-supervised learning for classification and regression, transductive learning, semi-supervised clustering, multi-instance learning, weak label learning, policy learning in partially observable environments, etc.

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013, held in Beijing, China, in July/August 2013. The 111 papers presented were carefully peer-reviewed and selected from 390 submissions. Topics covered include information theoretic and Bayesian approaches; probabilistic graphical models; pattern recognition and computer vision; signal processing and image processing; machine learning and computational intelligence; neural networks and neuro-informatics; statistical inference and uncertainty reasoning; bioinformatics and computational biology and speech recognition and natural language processing.

This book constitutes the proceedings of the 14th Pacific-Rim Conference on Multimedia, PCM 2013, held in Nanjing, China, in December 2013. The 30 revised full papers and 27 poster papers presented were carefully reviewed and selected from 153 submissions. The papers cover a wide range of topics in the area of multimedia content analysis, multimedia signal processing and communications and multimedia applications and services.

This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The 34 revised papers presented together with two invited papers were carefully reviewed and selected from 59 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.

This book constitutes the proceedings of the Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, IScIDE 2011, held in Xi'an, China, in October 2011. The 97 papers presented were carefully peer-reviewed and selected from 389 submissions. The IScIDE papers in this volume are organized in topical sections on machine learning and computational intelligence; pattern recognition; computer vision and image processing; graphics and computer visualization; knowledge discovering, data mining, web mining; multimedia processing and application.

Selected, peer reviewed papers from the 2011 International Conference on Mechanics and Manufacturing Systems (ICMMS 2011), November 13-14, 2011, Ningbo, China

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book cover

Praise for the First Edition'... the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students.'—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.

The First Asian Conference on Machine Learning (ACML 2009) was held at Nanjing, China during November 2–4, 2009.This was the?rst edition of a series of annual conferences which aim to provide a leading international forum for researchers in machine learning and related?elds to share their new ideas and research?ndings. This year we received 113 submissions from 18 countries and regions in Asia, Australasia, Europe and North America. The submissions went through a r- orous double-blind reviewing process. Most submissions received four reviews, a few submissions received?ve reviews, while only several submissions received three reviews. Each submission was handled by an Area Chair who coordinated discussions among reviewers and made recommendation on the submission. The Program Committee Chairs examined the reviews and meta-reviews to further guarantee the reliability and integrity of the reviewing process. Twenty-nine - pers were selected after this process. To ensure that important revisions required by reviewers were incorporated into the?nal accepted papers, and to allow submissions which would have - tential after a careful revision, this year we launched a “revision double-check” process. In short, the above-mentioned 29 papers were conditionally accepted, and the authors were requested to incorporate the “important-and-must”re- sionssummarizedbyareachairsbasedonreviewers'comments.Therevised?nal version and the revision list of each conditionally accepted paper was examined by the Area Chair and Program Committee Chairs. Papers that failed to pass the examination were?nally rejected.

The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is one of the preeminent international conferences on artificial intelligence (AI). PRICAI 2008 (http://www.jaist.ac.jp/PRICAI-08/) was the tenth in this series of biennial int- national conferences highlighting the most significant contributions to the field of AI. The conference was held during December 15–19, 2008, in the beautiful city Hanoi, the capital of Vietnam. As in previous years this year's technical program saw very high standards in both the submission and paper review process, resulting in an exciting program that reflects the great variety and depth of modern AI research. This year's contributions covered all traditional areas of AI, including AI foundations, knowledge representation, knowledge acquisition and ontologies, evolutionary computation, etc., as well as va- ous exciting and innovative applications of AI to many different areas. There was particular emphasis in the areas of machine learning and data mining, intelligent agents, language and speech processing, information retrieval and extraction.

This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.

This book constitutes the thoroughly refereed post-proceedings of three workshops and an industrial track held in conjunction with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China in May 2007. The 62 revised full papers presented together with an overview article to each workshop were carefully reviewed and selected from 355 submissions.

An important role of diagnostic medicine research is to estimate and compare the accuracies of diagnostic tests. This book provides a comprehensive account of statistical methods for design and analysis of diagnostic studies, including sample size calculations, estimation of the accuracy of a diagnostic test, comparison of accuracies of competing diagnostic tests, and regression analysis of diagnostic accuracy data. Discussing recently developed methods for correction of verification bias and imperfect reference bias, methods for analysis of clustered diagnostic accuracy data, and meta-analysis methods, Statistical Methods in Diagnostic Medicine explains: • Common measures of diagnostic accuracy and designs for diagnostic accuracy studies • Methods of estimation and hypothesis testing of the accuracy of diagnostic tests • Meta-analysis • Advanced analytic techniques-including methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting when an imperfect gold standard is used Thoroughly detailed with numerous applications and end-of-chapter problems as well as a related FTP site providing FORTRAN program listings, data sets, and instructional hints, Statistical Methods in Diagnostic Medicine is a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.