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Methods and Applications of Artificial Intelligence
Arti?cial intelligence has attracted a renewed interest from distinguished sci- tists and has again raised new, more realistic this time, expectations for future advances regarding the development of theories, models and techniques and the use of them in applications pervading many areas of our daily life. The borders of human-level intelligence are still very far away and possibly unknown. Nev- theless, recent scienti?c work inspires us to work even harder in our exploration of the unknown lands of intelligence. This volume contains papers selected for presentation at the 3rd Hellenic Conference on Arti?cial Intelligence (SETN 2004), the o?cial meeting of the Hellenic Society for Arti?cial ...
Machine Learning
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
Progress in Artificial Intelligence. Knowledge Extraction, Multi-agent Systems, Logic Programming, and Constraint Solving
This book constitutes the refereed proceedings of the 10th Portuguese Conference on Artificial Intelligence, EPTA 2001, held in Porto, Portugal, in December 2001. The 21 revised long papers and 18 revised short papers were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections on extraction of knowledge from databases, AI techniques for financial time series analysis, multi-agent systems, AI logics and logic programming, constraint satisfaction, and AI planning.
Advances in Intelligent Data Analysis VI
This book constitutes the refereed proceedings of the 6th International Conference on Intelligent Data Analysis, IDA 2005, held in Madrid, Spain in September 2005. The 46 revised papers presented together with two tutorials and two invited talks were carefully reviewed and selected from 184 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.
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...
A Life in Stages
“The station where you begin your life, does not need to be your station at the end of your life. The choice is yours.” Growing up poor in wartime England, Frank Farr is an indifferent student, and once he gets into his rebellious teen years, struggles with juvenile delinquency and seems headed for trouble. But when he is presented with the opportunity of a place in a boarding school for “bright delinquents” Frank gets mentored into a passion for learning...and his life takes an entirely different direction. “From shop floor to board room” Beginning as an assistant in a Canadian supermarket, Frank’s willingness to work hard and to continuously learn, starts him on an ascent to the highest levels of the corporate retail world, a happy marriage, fatherhood, and travels around the world. “Good Luck is where preparation meets opportunity” A Life in Stages follows Frank’s rise from unpromising beginnings to eighty-two years of living a good life, learning, working hard and enjoying the love of family and the companionship of friends and colleagues.
Representation Learning
This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.
