Showing papers by "Jerome H. Friedman published in 1994"
01 Jan 1994
TL;DR: The underlying principles of many of the practical approaches developed in artificial intelligence and connectionism have been reviewed, with the goal of placing them in a common perspective and providing a unifying overview.
Abstract: Predictive learning has been traditionally studied in applied mathematics (function approximation), statistics (nonparametric regression), and engineering (pattern recognition). Recently the fields of artificial intelligence (machine learning) and connectionism (neural networks) have emerged, increasing interest in this problem, both in terms of wider application and methodological advances. This paper reviews the underlying principles of many of the practical approaches developed in these fields, with the goal of placing them in a common perspective and providing a unifying overview.
216 citations
01 Jan 1994
TL;DR: The underlying principles of many of the practical approaches developed in artificial intelligence and connectionism have been reviewed, with the goal of placing them in a common perspective and providing a unifying overview.
Abstract: Predictive learning has been traditionally studied in applied mathematics (function approximation), statistics (nonparametric regression), and engineering (pattern recognition). Recently the fields of artificial intelligence (machine learning) and connectionism (neural networks) have emerged, increasing interest in this problem, both in terms of wider application and methodological advances. This paper reviews the underlying principles of many of the practical approaches developed in these fields, with the goal of placing them in a common perspective and providing a unifying overview.
81 citations