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Note: Links lead to the DBLP on the Web. Douglas H. Fisher Lemuel R. Waitman , Douglas H. Fisher, Paul H. King : Bootstrapping Rule Induction. ICDM 2003 : 677-680 Douglas H. Fisher: Editorial. Machine Learning 47 (1): 5-6 (2002) Frank Hoffmann , David J. Hand , Niall M. Adams , Douglas H. Fisher, Gabriela Guimarães : Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001, Proceedings Springer 2001 Douglas H. Fisher: Introduction. Machine Learning 42 (1/2): 5-7 (2001) Douglas A. Talbert , Douglas H. Fisher: An empirical analysis of techniques for constructing and searching k-dimensional trees. KDD 2000 : 26-33 David J. Hand , Douglas H. Fisher, Michael R. Berthold : Advances in intelligent data analysis. Intell. Data Anal. 4 (2): 93-95 (2000) Douglas A. Talbert , Douglas H. Fisher: OPT-KD: An Algorithm for Optimizing Kd-Trees. ICML 1999 : 398-405 Douglas A. Talbert , Douglas H. Fisher: Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees. IDA 1999 : 407-414 Lewis Frey , Cen Li , Douglas A. Talbert , Douglas H. Fisher: A Review of the Fourteenth International Conference on Machine Learning. Intell. Data Anal. 2 (1-4): 245-255 (1998) Douglas H. Fisher: Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), Nashville, Tennessee, USA, July 8-12, 1997 Morgan Kaufmann 1997 Douglas H. Fisher: Iterative Optimization and Simplification of Hierarchical Clusterings. JAIR 4 : 147-178 (1996) Bob Evans , Douglas H. Fisher: Overcoming Process Delays with Decision Tree Induction. IEEE Expert 9 (1): 60-66 (1994) Douglas H. Fisher, Ling Xu , James R. Carnes , Yoram Reich , Steven J. Fenves , Jason Chen , Richard Shiavi , Gautam Biswas , Jerry B. Weinberg : Applying AI Clustering to Engineering Tasks. IEEE Expert 8 (6): 51-60 (1993) Douglas H. Fisher, Ling Xu , Nazih Zard : Ordering Effects in Clustering. ML 1992 : 162-168 Douglas H. Fisher, Jungsoon P. Yoo : Combining Evidence of Deep and Surface Similarity. ML 1991 : 46-50 Jungsoon P. Yoo , Douglas H. Fisher: Identifying Cost Effective Boundaries of Operationality. ML 1991 : 569-573 Hua Yang , Douglas H. Fisher, Hubertus Franke : Improving Planning Efficient by Conceptual Clustering. IEA/AIE (Vol. 2) 1990 : 942-948 Brian M. Carlson , Jerry B. Weinberg , Douglas H. Fisher: Search Control, Utility, and Concept Induction. ML 1990 : 85-92 Douglas H. Fisher, Philip K. Chan : Statistical guidance in symbolic learning. Ann. Math. Artif. Intell. 2 : 135-147 (1990) Douglas H. Fisher, Kathleen B. McKusick : An Empirical Comparison of ID3 and Back-propagation. IJCAI 1989 : 788-793 Douglas H. Fisher: Noise-Tolerant Conceptual Clustering. IJCAI 1989 : 825-830 Douglas H. Fisher, Kathleen B. McKusick , Raymond J. Mooney , Jude W. Shavlik , Geoffrey G. Towell : Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML 1989 : 169-173 Hua Yang , Douglas H. Fisher: Conceptual Clustering of Mean-Ends Plans. ML 1989 : 232-234 Jungsoon P. Yoo , Douglas H. Fisher: Conceptual Clustering of Explanations. ML 1989 : 8-10 John H. Gennari , Pat Langley , Douglas H. Fisher: Models of Incremental Concept Formation. Artif. Intell. 40 (1-3): 11-61 (1989) Douglas H. Fisher: A Computational Account of Basic Level and Typicality Effects. AAAI 1988 : 233-238 Douglas H. Fisher, Jeffrey C. Schlimmer : Concept Simplification and Prediction Accuracy. ML 1988 : 22-28 Douglas H. Fisher: Improving Inference through Conceptual Clustering. AAAI 1987 : 461-465 Douglas H. Fisher: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2 (2): 139-172 (1987) Jeffrey C. Schlimmer , Douglas H. Fisher: A Case Study of Incremental Concept Induction. AAAI 1986 : 496-501 Douglas H. Fisher, Pat Langley : Approaches to Conceptual Clustering. IJCAI 1985 : 691-697 1 [ 29 ] 2 [ 26 ] 3 [ 19 ] 4 [ 14 ] 5 [ 19 ] 6 [ 13 ] 7 [ 19 ] 8 [ 20 ] 9 [ 19 ] 10 [ 15 ] 11 [ 23 ] 12 [ 7 ] 13 [ 29 ] 14 [ 26 ] [ 29 ] 15 [ 29 ] 16 [ 31 ] 17 [ 1 ] [ 7 ] 18 [ 23 ] 19 [ 10 ] [ 12 ] 20 [ 10 ] 21 [ 19 ] 22 [ 2 ] [ 5 ] 23 [ 10 ] 24 [ 19 ] 25 [ 23 ] [ 24 ] [ 25 ] [ 27 ] 26 [ 10 ] 27 [ 31 ] 28 [ 14 ] [ 19 ] 29 [ 18 ] [ 19 ] 30 [ 9 ] [ 15 ] 31 [ 8 ] [ 16 ] [ 17 ] 32 [ 18 ]  ©2004 Association for Computing Machinery |
