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Machine Learning for Data Science Handbook
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Utilizing Problem Structure in Planning
Planning is a crucial skill for any autonomous agent, be it a physically embedded agent, such as a robot, or a purely simulated software agent. For this reason, planning, as a central research area of artificial intelligence from its beginnings, has gained even more attention and importance recently. After giving a general introduction to AI planning, the book describes and carefully evaluates the algorithmic techniques used in fast-forward planning systems (FF), demonstrating their excellent performance in many wellknown benchmark domains. In advance, an original and detailed investigation identifies the main patterns of structure which cause the performance of FF, categorizing planning domains in a taxonomy of different classes with respect to their aptitude for being solved by heuristic approaches, such as FF. As shown, the majority of the planning benchmark domains lie in classes which are easy to solve.
Machine Learning: ECML 2004
The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. W...
Reducing Planning Problems by Path Reduction
Planning is among the characteristic features of intelligence and therefore it is a central research topic of Intellectics since its beginning. Although planning is a very hard task, recent planning systems have achieved an astonishing performance and are applied in various fields. One reason for the success of these systems lies, among others, in the exploitation of structural properties that are present in many but not all problems. The use of such structural properties therefore leads to a specialization on a class of problems. Their exploitation is often conducted by a preprocessing step, i.e., by the application of a special algorithm prior to the search for a plan. This work identifies and examines the class of c-invariants as such a structural property of planning problems. c-Invariants are state invariants and are present in many problems of practical interest. Building on the features of c-invariants, the dissertation presents path reduction, a preprocessing technique that can significantly simplify planning problems. Finally, the work describes an implementation of path reduction and examines its application.
Artificial Intelligence: Methodology, Systems, and Applications
This book constitutes the refereed proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Appliations, AIMSA 2002, held in Varna, Bulgaria in September 2002. The 26 revised full papers presented together with 2 invited papers were carefully reviewed and selected for inclusion in this book. The papers address a broad spectrum of topics in AI, including natural language processing, computational learning, Machine learning, AI planning, heuristics, neural information processing, adaptive systems, computational linguistics, multi-agent systems, AI logic, knowledge management, and information retrieval.
Web-powered Databases
This text provides a snapshot of contemporary research and development activities in the area of Web and Internet databases. It also provides case studies of successful Web database applications, including an online pay claim, a product catalogue and multiple-choice assessment through the Web.
Conceptual Structures: Knowledge Architectures for Smart Applications
Researchers and professionals in the relevant fields will find this book a must-read, as it defines the leading edge of current research into conceptual structures. It constitutes the refereed proceedings of the 15th International Conference on Conceptual Structures, held in Sheffield, UK in July 2007. With almost 50 papers contained in its 500 pages, it includes a special focus on the application of conceptual structures in business and technological settings and is organized into topical sections for ease of reference.
Machine Learning and Knowledge Discovery in Databases, Part III
This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
Exploring Religious Dimensions in AI and Humanity
Exploring Religious Dimensions in A.I. and Humanity explores how the phenomenal advancements of artificial intelligence can reshape our spiritual journeys and influence established ethical frameworks. Very Reverend Dr. Aristarchos Gkrekas offers insights grounded in deep religious understanding. Nicholas Kokkinos provides insightful analyses of the ethical implications of emerging technologies and the potential for AI to serve as a dynamic repository of religious knowledge. Through years of collaboration, both authors sought a common approach to navigate the exponential growth of technology and its impact on humanity's enduring questions about consciousness, ethics, and spirituality. This book is an essential read for those intrigued by the evolving dialogue between technology and spirituality and is enriched by references to centuries-old texts. It promises to ignite curiosity and spark a deeper understanding of our digital age's spiritual dimensions and the ethical questions that arise from these phenomenal developments.
Advances in Informatics
This volume contains a subset of the papers presented at the 10th Panhellenic Conference in Informatics (PCI 2005), which took place at the City of Volos, Greece, during November 11–13, 2005. After an international call for papers, 252 full papers were submitted. The number of the submitted papers constitutes a record number for the conf- ence and reveals its growing dynamics. The authors represented universities and institutes from the following countries: Algeria, Bulgaria, China, Cyprus, Czech Republic, Finland, Greece, The Netherlands, Hungary, Italy, Japan, Korea, The Kingdom of Saudi Arabia, Lebanon, Lithuania, Malaysia, Poland, Romania, Spain, Taiwan, Turkey, Ukraine, UK, and USA. O...
