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Dennis F. Kibler
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- affiliation: University of California, Irvine, USA
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2000 – 2009
- 2005
- [j15]Bob Y. Chan
, Dennis F. Kibler:
Using hexamers to predict cis-regulatory motifs in Drosophila. BMC Bioinform. 6: 262 (2005) - 2002
- [j14]Steven Hampson, Dennis F. Kibler, Pierre Baldi:
Distribution patterns of over-represented k-mers in non-coding yeast DNA. Bioinform. 18(4): 513-528 (2002) - 2000
- [j13]Yuh-Jyh Hu, Suzanne B. Sandmeyer, Calvin McLaughlin, Dennis F. Kibler:
Combinatorial motif analysis and hypothesis generation on a genomic scale. Bioinform. 16(3): 222-232 (2000) - [j12]Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth:
The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explor. 2(2): 81-85 (2000) - [c28]Steven Hampson, Pierre Baldi, Dennis F. Kibler, Suzanne B. Sandmeyer:
Analysis of Yeast's ORF Upstream Regions by Parallel Processing, Microarrays, and Computational Methods. ISMB 2000: 190-201
1990 – 1999
- 1999
- [j11]Dennis F. Kibler:
Paul R. Cohen's Empirical Methods for Artificial Intelligence. Artif. Intell. 113(1-2): 281-284 (1999) - [j10]Steven Hampson, Dennis F. Kibler:
Minimum Generalization Via Reflection: A Fast Linear Threshold Learner. Mach. Learn. 37(1): 51-73 (1999) - [c27]Yuh-Jyh Hu, Suzanne B. Sandmeyer, Dennis F. Kibler:
Detecting Motifs from Sequences. ICML 1999: 181-190 - 1998
- [j9]Dennis F. Kibler:
A Review of Machine Learning. AI Mag. 19(1): 136 (1998) - 1997
- [c26]Piew Datta, Dennis F. Kibler:
Symbolic Nearest Mean Classifiers. AAAI/IAAI 1997: 82-87 - [c25]Piew Datta, Dennis F. Kibler:
Learning Symbolic Prototypes. ICML 1997: 75-82 - 1996
- [c24]Yuh-Jyh Hu, Dennis F. Kibler:
Generation of Attributes for Learning Algorithms. AAAI/IAAI, Vol. 1 1996: 806-811 - 1995
- [c23]Piew Datta, Dennis F. Kibler:
Learning Prototypical Concept Descriptions. ICML 1995: 158-166 - 1993
- [j8]David Ruby, Dennis F. Kibler:
Learning Steppingstones for Problem Solving. Int. J. Pattern Recognit. Artif. Intell. 7(3): 527-540 (1993) - [c22]Steven E. Hampson, Dennis F. Kibler:
Large plateaus and plateau search in Boolean Satisfiability problems: When to give up searching and start again. Cliques, Coloring, and Satisfiability 1993: 437-455 - [c21]Piew Datta, Dennis F. Kibler:
Concept Sharing: A Means to Improve Multi-Concept Learning. ICML 1993: 89-96 - 1992
- [j7]Michael J. Pazzani, Dennis F. Kibler:
The Utility of Knowledge in Inductive Learning. Mach. Learn. 9: 57-94 (1992) - [c20]David Ruby, Dennis F. Kibler:
Learning Episodes for Optimization. ML 1992: 379-384 - 1991
- [j6]David W. Aha, Dennis F. Kibler, Marc K. Albert:
Instance-Based Learning Algorithms. Mach. Learn. 6: 37-66 (1991) - [c19]David Ruby, Dennis F. Kibler:
SteppingStone: An Empirical and Analytical Evaluation. AAAI 1991: 527-532 - 1990
- [c18]David Ruby, Dennis F. Kibler:
Learning steppingstones for problem solving. TAI 1990: 237-244
1980 – 1989
- 1989
- [j5]Dennis F. Kibler, David W. Aha, Marc K. Albert:
Instance-based prediction of real-valued attributes. Comput. Intell. 5: 51-57 (1989) - [c17]David Rudy, Dennis F. Kibler:
Learning to Plan in Complex Domains. ML 1989: 180-182 - [c16]David Ruby, Dennis F. Kibler:
Learning Subgoal Sequences for Planning. IJCAI 1989: 609-614 - [c15]David W. Aha, Dennis F. Kibler:
Noise-Tolerant Instance-Based Learning Algorithms. IJCAI 1989: 794-799 - 1988
- [c14]Dennis F. Kibler, David W. Aha:
Comparing Instance-Averaging with Instance-Filtering Learning Algorithms. EWSL 1988: 63-79 - [c13]Dennis F. Kibler, Pat Langley:
Machine Learning as an Experimental Science. EWSL 1988: 81-92 - 1987
- [c12]Dennis F. Kibler:
The Qualitative Character of Intelligence. ICDE 1987: 289-290 - 1986
- [j4]Bruce W. Porter, Dennis F. Kibler:
Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics. Mach. Learn. 1(3): 249-286 (1986) - 1985
- [j3]Rogers P. Hall, Dennis F. Kibler:
Differing Methodological Perspectives in Artificial Intelligence. AI Mag. 6(3): 166-178 (1985) - [j2]John S. Conery, Dennis F. Kibler:
AND Parallelism and Nondeterminism in Logic Programs. New Gener. Comput. 3(1): 43-70 (1985) - [c11]Dennis F. Kibler, John S. Conery:
Parallelism in AI Programs. IJCAI 1985: 53-56 - [c10]Bruce W. Porter, Dennis F. Kibler:
A Comparison of Analytic and Experimental Goal Regression for Machine Learning. IJCAI 1985: 555-559 - 1984
- [c9]Bruce W. Porter, Dennis F. Kibler:
Learning Operator Transformations. AAAI 1984: 278-282 - 1983
- [c8]Dennis F. Kibler, Bruce W. Porter:
Episodic Learning. AAAI 1983: 191-196 - [c7]Dennis F. Kibler, Bruce W. Porter:
Perturbation: A Means for Guiding Generalization. IJCAI 1983: 415-418 - [c6]John S. Conery, Dennis F. Kibler:
AND Parallelism in Logic Programs. IJCAI 1983: 539-543 - 1981
- [c5]John S. Conery, Dennis F. Kibler:
Parallel interpretation of logic programs. FPCA 1981: 163-170 - [c4]Dennis F. Kibler, Paul Morris:
Don't Be Stupid. IJCAI 1981: 345-347
1970 – 1979
- 1977
- [c3]Dennis F. Kibler, James M. Neighbors, Thomas A. Standish:
Program manipulation via an efficient production system. Artificial Intelligence and Programming Languages 1977: 163-173 - 1976
- [j1]Jack Sklansky, Dennis F. Kibler:
A Theory of Nonuniformly Digitized Binary Pictures. IEEE Trans. Syst. Man Cybern. 6(9): 637-647 (1976) - [c2]Burt M. Leavenworth, Raymond J. Hookway, George W. Ernst, Thomas A. Standish, Dennis F. Kibler, James M. Neighbors, J. C. Huang:
SICSOFT (Paper Session). ACM Annual Conference 1976: 498 - [c1]Thomas A. Standish, Dennis F. Kibler, James M. Neighbors:
Improving and refining programs by program manipulation. ACM Annual Conference 1976: 509-516
Coauthor Index
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