Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

The emphasis of the book is on the question of Why–only if why an algorithm is successful is
understood, can it be properly applied, and the results trusted. Algorithms are often taught …

Current machine learning techniques have achieved great success; however, there are
many deficiencies. First, to train a strong model, a large amount of training examples are …

It is well understood from literature that the performance of a machine learning (ML) model is
upper bounded by the quality of the data. While researchers and practitioners have focused …

This essay gives advice to authors of papers on machine learning, although much of it
carries over to other computational disciplines. The issues covered include the material that …

Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today's most rapidly growing technical fields …

The evaluation of classifiers or learning algorithms is not a topic that has, generally, been
given much thought in the fields of Machine Learning and Data Mining. More often than not …

The increasing interest in data mining, or the use of historical data to discover regularities
and improve future decisions, follows from the confluence of several recent trends: the falling …

Machine Learning according to Michie et al (D. Michie, 1994) is generally taken to
encompass automatic computing procedures based on logical or binary operations that …

Breakthroughs in machine learning are rapidly changing science and society, yet our
fundamental understanding of this technology has lagged far behind. Indeed, one of the …