References
- Barlow H B. Unsupervised learning. Neural Comput. 1989; 1: 295–311
- Bridle J S. Alpha-nets: a recurrent ‘neural’ network architecture with a hidden Markov model interpretation. Speech Commun. 1990; 9: 83–92
- Carpenter G A, Grossberg S. The adaptive resonance theory of adaptive pattern recognition by a self-organising neural network. Computer March, 1988; 77–88
- Dayan P, Hinton G. Varieties of Helmholtz machine. Neural Networks 1996; 9: 1385–403
- Field D J. What is the goal of sensory coding?. Neural Comput. 1994; 6: 559–601
- Foldiak P. Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 1990; 64: 165–70
- Friedman J H, Tukey J W. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. 1974; C-23: 881–90
- Fukushima K, Miyake S. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recogn. 1982; 15: 455–69
- Hebb D O. The Organisation of Behavior. Wiley, New York 1949
- Hinton G, Zemel R. Autoencoders, minimum description-length and Helmholtz free energy. Advances in Neural Information Processing Systems, J Cowan, et al. Morgan Kaufmann, San Mateo, CA 1994; 6: 3–10
- Huber P J. Projection pursuit. Ann. Stat. 1985; 13: 435–75
- Intrator N. Feature extraction using an unsupervised neural network. Neural Comput. 1992; 4: 98–107
- Iwamida H, Katagiri S, McDermott E, Tohkura Y (1990) A hybrid speech recognition system using HMMs with an LVQ-trained codebook. 1990 Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Albuquerque, NM, April, 1990. IEEE, Piscataway, NJ, 489–92
- Kohonen T (1982) Clustering taxonomy and topological maps of patterns. Proc. 6th IEEE Int. Conf. on Pattern Recognition, Munich, October, 1982. IEEE, Piscataway, NJ, 114–28
- Kohonen T, Torkkola K, Shozakai M, Kangas J, Ventä O (1988) Phonetic typewriter for Finnish and Japanese. Proc., 1988, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, New York, April, 1988. IEEE, Piscataway, NJ, 607–10
- Luttrell S P. Self-supervised adaptive networks. IEE Proc. F 1992; 139: 371–7
- McDermott E, Katagiri S (1989) Shift-invariant, multi-category phoneme recognition using Kohonen's LVQ2. Proc., 1989, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Glasgow, May, 1989. IEEE, Piscataway, NJ, 81–4
- Nowell P, Moore R K (1995) The application of dynamic programming techniques to non-word based topic spotting. Proc. 4th Eur. Conf. on Speech Communications and Technology, Madrid, September, 1995, J M Pardo, et al. 1355–8, (ESCA)
- Oja E. A simplified neuron model as a principal component analyser. J. Math. Biol. 1982; 15: 267–73
- Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 1996; 381: 607–9
- Robinson A J. An application of recurrent nets to phone probability estimation. IEEE Trans. Neural Networks 1994; 5: 229–39
- Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation. Parallel Distributed Processing. MIT Press, Cambridge, MA 1986; 1
- Russell M J, Moore R K, Tomlinson M J, Deacon J C A. RSRE speech database recordings. Part II: Recordings made for automatic speech recognition assessment and research. MalvernUK 1983, RSRE report 84008, DRA
- Sankoff D, Kruskal J B. An anthology of algorithms and concepts for sequence comparison. Time Warps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparison. Addison-Wesley, Reading, MA 1983
- Saund E. A multiple cause mixture model for unsupervised learning. Neural Comput. 1994; 7: 57–71
- Skilling A I, Nowell P, Moore R K. Acoustic based topic-spotting. 1994, unpublished report
- Tattersall G D, Johnston R D. Self-organising arrays for speech recognition. Proc. Inst. Acoust. 1984; 6: 323–31
- Webber C J S. Self-organisation of transformation-invariant detectors for constituents of perceptual patterns. Network: Comput. Neural Syst. 1994; 5: 471–96
- Yu G, Russel W, Schwartz R, Makhoul J (1990) Discriminant analysis and supervised vector quantisation for continuous speech recognition. Proc., 1990, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Albuquerque, NM, April, 1990. IEEE, Piscataway, NJ, 685–8
- Zemel R. A minimum description-length framework for unsupervised learning. University of Toronto. 1993, PhD Thesis
- Ziv J, Lempel A. A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 1977; IT-23: 337–43