Machine Learning Proceedings 1994

This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning. Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes. This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions. The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.

Machine Learning Proceedings 1994.

Machine Learning Proceedings 1995

Information retrieval (IR) is becoming an increasingly important area as scientific, business and government organisations take up the notion of "information superhighways" and make available their full text databases for searching. Containing a selection of 35 papers taken from the 17th Annual SIGIR Conference held in Dublin, Ireland in July 1994, the book addresses basic research and provides an evaluation of information retrieval techniques in applications. Topics covered include text categorisation, indexing, user modelling, IR theory and logic, natural language processing, statistical and probabilistic models of information retrieval systems, routing, passage retrieval, and implementation issues.

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.