Title :
Machine Learning: The State of the Art
Author :
Wang, Jue ; Tao, Qing
Abstract :
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which implementation of data models and data sets rely. A newly discovered challenge to ML is the Rashomon effect, which means that data are possibly generated from a mixture of heterogeneous sources. A simple classification standard can shed light on emerging forms of ML. This article is part of a special issue on AI in China.
Keywords :
learning (artificial intelligence); algorithm design; classification standard; machine learning; statistical analysis; Rashomon effect; algorithm design; feature selection; learning to rank; machine learning; manifold learning; metric learning; multi-instance learning; nonlinear backpropagation; perceptron; relational learning; rule + exception learning; semisupervised learning; statistical analysis; statistical learning methods; structural learning; supervised learning; symbolic learning methods; unsupervised learning;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2008.107
