Unsupervised learning and generalization

The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation.

[1]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[2]  J. E. Jackson A User's Guide to Principal Components , 1991 .

[3]  S. Strother,et al.  Scaled Subprofile Model: A Statistical Approach to the Analysis of Functional Patterns in Positron Emission Tomographic Data , 1987, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  Jun Zhang,et al.  A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-Based Image Segmentation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Anil K. Jain,et al.  Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition , 1997, IEEE Trans. Neural Networks.

[6]  Andreas S. Weigend,et al.  John A. Hertz, Anders S. Krogh, Richard G. Palmer, Introduction to the Theory of Neural Computation , 1993, Artif. Intell..

[7]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[8]  Yiu-Fai Wong,et al.  Clustering Data by Melting , 1993, Neural Computation.

[9]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[10]  Lars Kai Hansen,et al.  On design and evaluation of tapped-delay neural network architectures , 1993, IEEE International Conference on Neural Networks.

[11]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[12]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[13]  Jan Larsen,et al.  DESIGN OF NEURAL NETWORK FILTERS , 1996 .

[14]  H. Akaike Fitting autoregressive models for prediction , 1969 .

[15]  Joachim M. Buhmann,et al.  Complexity Optimized Data Clustering by Competitive Neural Networks , 1993, Neural Computation.