الصفحة 1
الصفحة 1
Theory and Applications of Models of Computation ; 16th International Conference, TAMC 2020, Changsha, China, October 18–20, 2020, Proceedings
This book constitutes the refereed proceedings of the 16th Annual Conference on Theory and Applications of Models of Computation, TAMC 2020, held in Changsha, China, in October 2020. The 37 full papers were carefully reviewed and selected from 83 submissions. The main themes of the selected papers are computability, complexity, algorithms, information theory and their extensions to machine learning theory and foundations of artificial intelligence.
Support Vector Machines : Theory and Applications
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in the respective fields.
Support Vector Machines
Explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and their computational efficiency compared to several other methods.The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field.The book covers all important topics concerning support vector machines such as: loss functions and their role in the learning process; reproducing kernel Hilbert spaces and their properties; a thorough statistical analysis that uses both traditional uniform bounds and more advanced localized techniques based on Rademacher averages and Talagrand's inequality; a detailed treatment of classification and regression; a detailed robustness analysis; and a description of some of the most recent implementation techniques.
Representation learning for natural language processing
Provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions..
Representation Learning for Natural Language Processing
This book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts.
Recent Advances in Formal Languages and Applications
This volume contains areas, mainly applications, which have not appeared in any collection of this type. The book representing "a gate to formal language theory and its applications", will be useful as a general source of information in computation theory, both at the undergraduate and research level.
Philosophical Perspectives on Lifelong Learning
The aim of this book is provide an easily accessible, practical yet scholarly source of information about the international concern for the philosophy, theory, categories and concepts of lifelong learning. The books is concerned to examine in depth the range of philosophical perspectives in the field of lifelong learning theory, policy, practice and applied scholarship, extending the scale and scope of the substantive contribution made by philosophical and theoretical approaches to our understanding of education.
Pattern Recognition and Image Analysis ; Vol.3687 : 3rd International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22-25, 2005, Part II
Constitutes the refereed proceedings of the Third International Conference on Advances in Pattern Recognition, ICAPR 2005, held in Bath, UK in August 2005. This title contains 87 contributions related to Pattern Recognition and Image Analysis. It deals with security and surveillance, biometrics, image processing and medical imaging
Object detection with deep learning models : Principles and applications
Discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval / A structured overview of deep learning in object detection / A diversified collection of applications of object detection using deep neural networks / Emphasize agriculture and remote sensing domains / Exclusive discussion on moving object detection
New Developments in Parsing Technology
Parsing can be defined as the decomposition of complex structures into their constituent parts, and parsing technology as the methods, the tools, and the software to parse automatically. Parsing is a central area of research in the automatic processing of human language. Parsers are being used in many application areas, for example question answering, extraction of information from text, speech recognition and understanding, and machine translation. New developments in parsing technology are thus widely applicable. This book contains contributions from many of today's leading researchers in the area of natural language parsing technology. The contributors describe their most recent work and a diverse range of techniques and results. This collection provides an excellent picture of the current state of affairs in this area. This volume is the third in a series of such collections, and its breadth of coverage should make it suitable both as an overview of the current state of the field for graduate students, and as a reference for established researchers.
Information theory and machine learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges.
Inductive logic programming ; 15th International Conference, ILP 2005, Bonn, Germany, August 10-13, 2005, Proceedings
“Change is inevitable.” Embracing this quote we have tried to carefully exp- iment with the format of this conference, the 15th International Conference on Inductive Logic Programming, hopefully making it even better than it already was. But it will be up to you, the inquisitive reader of this book, to judge our success. The major changes comprised broadening the scope of the conference to include more diverse forms of non-propositional learning, to once again have tutorials on exciting new areas, and, for the ?rst time, to also have a discovery challenge as a platform for collaborative work. This year the conference was co-located with ICML 2005, the 22nd Inter- tional Conference on Machine Learning, and also in close proximity to IJCAI 2005, the 19th International Joint Conference on Arti?cial Intelligence. - location can be tricky, but we greatly bene?ted from the local support provided by Codrina Lauth, Michael May, and others. We were also able to invite all ILP and ICML participants to shared events including a poster session, an invited talk, and a tutorial about the exciting new area of “statistical relational lea- ing”. Two more invited talks were exclusively given to ILP participants and were presented as a kind of stock-taking—?ttingly so for the 15th event in a series—but also tried to provide a recipe for future endeavours.
Induction, Algorithmic Learning Theory, and Philosophy
This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays provide engaging surveys of different, complementary, and mutually inspiring approaches to the topic, both from a philosophical and a mathematical viewpoint.
Fundamentals of Artificial Intelligence
Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
Feature Extraction : Foundations and Applications
This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. "This book compiles some very promising techniques, coming from an extremely smart collection of researchers, delivering their best ideas in a competitive environment."
Discovery Science ; Vol. 4265 ; 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings
This LNAI volume, containing the proceedings of the 9th International C- ference onDiscoveryScience, is structured in three parts. The ?rstpart contains the papers/abstracts of the invited talks, the second part contains the accepted long papers, and the third part the accepted regular (short) papers. Out of 87 submitted papers, 23 were accepted for publication as long papers, and 18 as regular papers. All the submitted papers were reviewed by two or three ref- ees. In addition to the presentations of accepted papers, the DS 2006 conference program consisted of three invited talks, two tutorials, the collocated ALT 2006 conference and the Pascal Dialogues workshop.
Discovery science ; Vol. 3735 ; 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings
This book constitutes the refereed proceedings of the 8th International Conference on Discovery Science, DS 2005, held in Singapore in October 2005, co-located with the International Conference on Algorithmic Learning Theory (ALT 2005). The 21 revised long papers and the 6 revised regular papers presented together with 9 project reports and 5 invited papers were carefully reviewed and selected from 112 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science.
Discovery Science ; 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on Discovery Science, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the 19th International Conference on Algorithmic Learning Theory, ALT 2008.
Discovery Science ; 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007. Proceedings
This volume consists of three parts. The first part contains the papers/abstracts of the invited talks, the second part contains the accepted long papers, and the third part contains the accepted regular papers.
Computer science : Theory and applications ; 15th International computer science symposium in Russia, CSR 2020, Yekaterinburg, Russia, June 29 – July 3, 2020, Proceedings
This book constitutes the proceedings of the 15th International Computer Science Symposium in Russia, CSR 2020, held in Yekaterinburg, Russia, in June 2020. The 25 full papers and 6 invited papers were carefully reviewed and selected from 49 submissions. The papers cover a broad range of topics, such as: algorithms and data structures; computational complexity, including hardness of approximation and parameterized complexity; randomness in computing, approximation algorithms, fixed-parameter algorithms; combinatorial optimization, constraint satisfaction, operations research; computational geometry; string algorithms; formal languages and automata, including applications to computational linguistics; codes and cryptography; combinatorics in computer science; computational biology; applications of logic to computer science, proof complexity; database theory; distributed computing; fundamentals of machine learning, including learning theory, grammatical inference and neural computing; computational social choice; quantum computing and quantum cryptography; theoretical aspects of big data.
