Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, held in Québec City, Canada in June 2006. The book presents 47 carefully reviewed, revised full papers. These are organized in topical sections on agents, bioinformatics, constraint satisfaction and distributed search, knowledge representation and reasoning, natural language, reinforcement learning and, supervised and unsupervised learning.
Encyclopedia of Machine Learning
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Lexical Variation and Change
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read at Oxford Academic and offered as a free PDF download from OUP and selected open access locations. This book introduces a systematic framework for understanding and investigating lexical variation, using a distributional semantics approach. Distributional semantics embodies the idea that the context in which a word occurs reveals the meaning of that word. In contemporary corpus linguistics, that idea takes shape in various types of quantitative analysis of the corpus contexts in which words appear. In this book, the authors explore how count-based token-level semantic vector ...
Data Mining for Social Network Data
Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in May 2013. The 17 regular papers and 15 short papers presented were carefully reviewed and selected from 73 initial submissions and are accompanied by 8 papers from the Graduate Student Symposium that were selected from 14 submissions. The papers cover a variety of topics within AI, such as: information extraction, knowledge representation, search, text mining, social networks, temporal associations.
Uncertainty Handling and Quality Assessment in Data Mining
The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.
Scientific and Technical Aerospace Reports
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Energy Systems Optimization Considering the Uncertainty of Future Developments
In light of anthropogenic climate change and the importance of energy to ensure high living standards, energy system optimization is used to explore different energy system layouts. A recent focus has been on determining cost-effective ways to mitigate greenhouse gas emissions. This work investigates how future uncertainties regarding technology costs influence optimization results. This is achieved through energy system optimization aimed at reducing system cost using stochastic optimization with probability distributions to capture expected future costs and uncertainties. Theoretical considerations and a minimal example energy system show that Jensen's inequality leads to an overestimation...
Data Science
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
Advances in Knowledge Discovery and Data Mining
This book constitutes the refereed proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, held in Hong Kong, China in April 2001. The 38 revised full papers and 22 short papers presented were carefully reviewed and selected from a total of 152 submissions. The book offers topical sections on Web mining, text mining, applications and tools, concept hierarchies, feature selection, interestingness, sequence mining, spatial and temporal mining, association mining, classification and rule induction, clustering, and advanced topics and new methods.
