S-TREE: self-organizing trees for data clustering and online vector quantization
暂无分享,去创建一个
[1] C.-K. Chan,et al. A complexity reduction technique for image vector quantization , 1992, IEEE Trans. Image Process..
[2] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[3] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .
[4] R.M. Gray,et al. A greedy tree growing algorithm for the design of variable rate vector quantizers [image compression] , 1991, IEEE Trans. Signal Process..
[5] William Equitz,et al. A new vector quantization clustering algorithm , 1989, IEEE Trans. Acoust. Speech Signal Process..
[6] N. J. A. Sloane,et al. Fast quantizing and decoding and algorithms for lattice quantizers and codes , 1982, IEEE Trans. Inf. Theory.
[7] Lai-Man Po,et al. Adaptive dimensionality reduction techniques for tree-structured vector quantization , 1994, IEEE Trans. Commun..
[8] E. Clothiaux,et al. Neural Networks and Their Applications , 1994 .
[9] D. Signorini,et al. Neural networks , 1995, The Lancet.
[10] Neil Davey,et al. A Comparative Study of two Self Organising and Structurally Adaptive Dynamic Neural Tree Networks , 1995 .
[11] Michel Verleysen,et al. Image compression by self-organized Kohonen map , 1998, IEEE Trans. Neural Networks.
[12] Jianmin Jiang,et al. Distortion equalized fuzzy competitive learning for image data vector quantization , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.
[13] Bernd Fritzke,et al. A Growing Neural Gas Network Learns Topologies , 1994, NIPS.
[14] Y. Y. Tang,et al. A structurally adaptive neural tree for the recognition of large character set , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.
[15] Kenneth Rose,et al. A non-greedy approach to tree-structured clustering , 1994, Pattern Recognit. Lett..
[16] Pasi Koikkalainen,et al. Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[17] Janos Racz,et al. Knowledge representation by dynamic competitive learning techniques , 1991, Defense + Commercial Sensing.
[18] Korris Fu-Lai Chung,et al. Fuzzy competitive learning , 1994, Neural Networks.
[19] Pasi Koikkalainen,et al. Progress with the Tree-Structured Self-Organizing Map , 1994, ECAI.
[20] Kaizhong Zhang,et al. A better tree-structured vector quantizer , 1991, [1991] Proceedings. Data Compression Conference.
[21] Allen M. Peterson,et al. Adaptive Vector Quantization Using a Self-Development Neural Network , 1990, IEEE J. Sel. Areas Commun..
[22] Joachim M. Buhmann,et al. Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis , 1997, NIPS.
[23] Joachim M. Buhmann,et al. Inferring Hierarchical Clustering Structures by Deterministic Annealing , 1996, KDD.
[24] Michael I. Jordan,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.
[25] Joachim M. Buhmann,et al. An Annealed "Neural Gas" Network for Robust Vector Quantization , 1996, ICANN.
[26] Robert M. Gray,et al. An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..
[27] Allen Gersho,et al. Asymptotically optimal block quantization , 1979, IEEE Trans. Inf. Theory.
[28] T. Landelius. Behavior Representation by Growing a Learning Tree , 1993 .
[29] Sang-Hui Park,et al. Self-creating and organizing neural networks , 1994, IEEE Trans. Neural Networks.
[30] L. Po,et al. Novel subspace distortion measurement for efficient implementation of image vector quantiser , 1990 .
[31] James C. Bezdek,et al. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Naonori Ueda,et al. A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers , 1994, Neural Networks.
[33] Wayne E. Stark,et al. Fine-coarse vector quantization , 1991, IEEE Trans. Signal Process..
[34] J. Makhoul,et al. Vector quantization in speech coding , 1985, Proceedings of the IEEE.
[35] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[36] Gerald Sommer,et al. Dynamic Cell Structures , 1994, NIPS.
[37] Chin-Chen Chang,et al. New tree-structured vector quantization with closest-coupled multipath searching method , 1997 .
[38] Chok-Ki Chan,et al. A fast method of designing better codebooks for image vector quantization , 1994, IEEE Trans. Commun..
[39] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[40] C.-C. Jay Kuo,et al. Fast tree-structured nearest neighbor encoding for vector quantization , 1996, IEEE Trans. Image Process..
[41] Robert M. Gray,et al. Speech coding based upon vector quantization , 1980, ICASSP.
[42] David William Pearson,et al. Applications of artificial neural networks , 1998 .
[43] Yiu-Fai Wong,et al. Clustering Data by Melting , 1993, Neural Computation.
[44] Wen-Tsuen Chen,et al. Image sequence coding using adaptive tree-structured vector quantization with multipath searching , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[45] Stanley C. Ahalt,et al. Competitive learning algorithms for vector quantization , 1990, Neural Networks.
[46] Olli Nevalainen,et al. On the splitting method for vector quantization codebook generation , 1997 .
[47] Allen Gersho,et al. Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.
[48] Joachim M. Buhmann,et al. Vector quantization with complexity costs , 1993, IEEE Trans. Inf. Theory.
[49] Pamela C. Cosman,et al. Tree-structured vector quantization with significance map for wavelet image coding , 1995, Proceedings DCC '95 Data Compression Conference.
[50] T. Adalı,et al. Learning Tree-Structured Vector Quantization for Image Compression , 1995 .
[51] Kenneth Rose,et al. Hierarchical, Unsupervised Learning with Growing via Phase Transitions , 1996, Neural Computation.
[52] Peter E. Hart,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[53] G H Ball,et al. A clustering technique for summarizing multivariate data. , 1967, Behavioral science.