|
Amsterdam J, 1988a. “Extending the Valiant learning model” In: Proceedings of 5th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Amsterdam J, 1988b. “Some philosophical problems with formal learning theory” In: Proceedings AAAl'88. Morgan-Kaufmann.
Google Scholar
|
|
Anderberg MR, 1973. Cluster Analysis for Applications. Academic Press, New York, NY.
Google Scholar
|
|
Angluin D and Laird PD, 1988. “Learning from noisy examples” In: Machine Learning2(4), 343–370.
Google Scholar
|
|
Angluin D and Smith CH, 1983. “Inductive inference: theory and methods” ACM Computing Surveys15(3), 237–269.
Google Scholar
|
|
Arbab B and Michie D, 1985. “Generating rules from examples” In: Proceedings 9th IJCAI'85. Morgan-Kaufmann.
Google Scholar
|
|
Baim PW, 1988. “A method for attribute selection in inductive learning systems” IEEE Transactions on Pattern Analysis and Machine Intelligence10(6), 888–896.
Google Scholar
|
|
Bierman AW and Feldman JA, 1972. “A survey of results in grammatical inference” In: Watanabe S (ed.), Frontiers of Pattern Recognition. Academic Press, New York, NY.
Google Scholar
|
|
Blumer A, Ehrenfeucht A, Haussler D and Warmuth M, 1986. “Classifying learnable geometric concepts with the Vapnik–Chervonenkis dimension” In: Proceedings 18th ACM Symposium. ACM.
Google Scholar
|
|
Blumer A, Ehrenfeucht A, Haussler D and Warmuth M, 1987. “Occam's razor” Information Processing Letters24(6), 377–380.
Google Scholar
|
|
Blythe J, 1988. “Constraining search in a hierarchical discriminative learning system” In: Proceedings ECAI'88. Pitman.
Google Scholar
|
|
Boucheron S and Sallantin J, 1988. “Learnability in the presence of noise” In: Proceedings EWSL'88. Pitman.
Google Scholar
|
|
Breiman L, Friedman JH, Olshen RA and Stone CJ, 1984. “Classification and regression trees” Wadsworth Int. Group, Belmont, CA.
Google Scholar
|
|
Buchanan BG and Mitchell TM, 1978. “Model directed learning of production rules” In: Waterman DA and Hayes-Roth F (eds.), Pattern Directed Inference Systems. Academic Press, New York, NY.
Google Scholar
|
|
Bundy A, Silver D and Plummer D, 1985. “An analytical comparison of some rule learning programs” Artificial Intelligence27(2), 137–181.
Google Scholar
|
|
Buntine R, 1989. “A Critique of the Valiant model” In Proceedings 11th IJCAI-89. Morgan-Kaufmann.
Google Scholar
|
|
Buntine W and Stirling KA, 1989. “Interactive induction” In: Hayes J and Michie D (eds.), Machine Intelligence12. Oxford University Press, Oxford.
Google Scholar
|
|
Carnap R, 1950. The Logical Foundations of Probability. Chicago.
Google Scholar
|
|
Carter C and Catlett J, 1987. “Assessing credit card applications using machine learning” IEEE Expert2(3), 71–79.
Google Scholar
|
|
Cestnik B, Kononenko I and Bratko I, 1987. “ASSISTANT'86: a knowledge elicitation tool for sophisticated users” In: Bratko I and Lavrac N (eds.), Progress in Machine Learning. Sigma Press, Wilmslow.
Google Scholar
|
|
Chan PK, 1989. “Inductive learning with BCT” In: Proceedings 6th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Chendrowska J, 1987. “PRISM: an algorithm for inducing modular rules” International Journal of Man-Machine Studies27(4), 349–370.
Google Scholar
|
|
Chow CK, 1957. “An optimum character recognition system using decision functions” IRE Transactions on Electronic ComputersEC-6 (121957), 247–254.
Google Scholar
|
|
Chrisman L, 1989. “Extending the Valiant framework to detect incorrect bias” Technical Report, School of Computer Science, Carnegie Mellon University, CMU-CS-89–137.
Google Scholar
|
|
Clark P and Niblett T, 1987. “Induction in noisy domains” In: Progress in Machine Learning. Sigma Press, Wilmslow.
Google Scholar
|
|
Devijver PA and Kittler J, 1982. Pattern Recognition: A Statistical Approach. Prentice-Hall, London.
Google Scholar
|
|
Dietterich TG and Michalski RS, 1985. “Discovering patterns in sequences of events” Artificial Intelligence25(2), 187–232.
Google Scholar
|
|
Drastal G, Meunier R and Raatz S, 1989. “Error correction in constructive induction” In: Proceedings 6th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Duda RO and Hart PE, 1973. Pattern Classification and Scene Analysis. Wiley, Chichester.
Google Scholar
|
|
Efron B, 1983. “Estimating the error rate of a prediction rule” Journal of the American Statistical Association78(382), 316–331.
Google Scholar
|
|
Feigenbaum EA, 1981. “Expert systems in the 1980s” In: Bond A (ed.), State of the Art Report on Machine Intelligence. Pergamon-Infotech, Maidenhead.
Google Scholar
|
|
Fisher DH, 1987. “Conceptual clustering, learning from examples and inference” In: Proceedings 4th International Conference on Machine Learning.Morgan-Kaufmann.
Google Scholar
|
|
Fu KS, 1974. Syntactic methods in Pattern Recognition. Academic Press, New York. NY.
Google Scholar
|
|
Gams M and Lavrac N, 1983. “Review of five empirical learning systems within a proposed schemata” In: Bratko I and Lavrac N (eds.), Progress in Machine Learning. Sigma Press, Wilmslow.
Google Scholar
|
|
Ganascia JG, 1987. “Learning with Hubert cubes” In: Bratko I and Lavrac N (eds.), Progress in Machine Learning. Sigma Press, Wilmslow.
Google Scholar
|
|
Glick N, 1978. “Additive estimators for probabilities of correct classification” Pattern Recognition10(3), 211–222.
Google Scholar
|
|
Gross KP, 1988. “Incremental multiple concept learning using experiments” In: Proceedings 5th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Haussler D, 1987. “Bias, version spaces and the Valiant learning Framework” In: Proceedings 4th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Haussler D, 1988a. “Quantifying inductive bias: AI learning algorithms and the Valiant learning framework” Artificial Intelligence36(2), 177–221.
Google Scholar
|
|
Haussler D, 1988b. “Space efficient learning algorithms” University of California, Santa Cruz, UCSC–CRL–88–2.
Google Scholar
|
|
Hayes-Roth F, 1974. “Schematic classification problems and their solution” Pattern Recognition6(2), 105–113.
Google Scholar
|
|
Highleyman WH, 1962. “The design and analysis of pattern recognition experiments” Bell Systems Technical Journal. 03.
Google Scholar
|
|
Iba W, Wogulis J and Langley P, 1988. “Trading off simplicity and coverage in incremental concept learning systems” In: Proceedings 5th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Jain AK and Dubes R, 1978. “Feature definition in pattern recognition with small sample size” Pattern Recognition10(2), 85–97.
Google Scholar
|
|
Kalkanis G, 1989. “A proposal to enhance decision tree based inductive systems” MSc Dissertation, University of Manchester Institute of Science and Technology.
Google Scholar
|
|
Kalkanis G, 1991. “The application of confidence interval error analysis to the design of decision tree classifiers” (To appear in Pattern Recognition Letters.)
Google Scholar
|
|
Kanal LN and Chandrasekaran B, 1971. “On dimensionality and sample size in statistic pattern recognition” Pattern Recognition3(3), 225–234.
Google Scholar
|
|
Kass GV, 1980. “An exploratory technique for investigating large quantities of categorical data” Applied Statistics29(2) 119–127.
Google Scholar
|
|
Kearns M, Li M, Pitt , Land Valiant L, 1987. “Recent results on Boolean concept learning” In: Proceedings 4th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Keynes JM, 1921. A Treatise on Probability. Macmillan.
Google Scholar
|
|
Kocabas S, 1991. “Knowledge representation and learning” Knowledge Engineering Review6(3).
Google Scholar
|
|
Kodratoff Y, 1988. Introduction to Machine Learning. Pitman.
Google Scholar
|
|
Langley P, Simon HA and Bradshaw GL, 1984. “Heuristics for empirical discovery” Technical Report, Carnegie Mellon University Robotics Institute.
Google Scholar
|
|
Lee WD and Ray SR, 1986. “Rule refinement using the probabilistic rule generator” In: Proceedings AAAI'86. Morgan-Kaufmann.
Google Scholar
|
|
Leith P, 1984. “Hierarchically structured production rules” The Computer Journal26(1), 1–5.
Google Scholar
|
|
Lenat DB, 1983. “The role of heuristics in learning by discovery: three case studies” In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An Artificial Intelligence Approach (vol. I) Tioga.
Google Scholar
|
|
Lewis PW, 1962. “The characteristic selection problem in recognition systems” IRE Transactions on Information Theory, IT–8 (021962), 171–178.
Google Scholar
|
|
Matheus CJ and Rendell LA, 1989. “Constructive induction on decision trees” In: Proceedings 11th IJCAI'89. Morgan-Kaufmann.
Google Scholar
|
|
Medin DI and Wattenmaker WD, 1986. “Categories, cohesiveness, theories and cognitive archaeology” In: Concepts Reconsidered: The Ecological and Intellectual Bases of Categories. Neisser U (ed.). Cambridge University Press.
Google Scholar
|
|
Mervis CB and Rosch E, 1981. “Categorisation and natural objects” Annual Review of Psychology, 32(2), 89–115.
Google Scholar
|
|
Michalski RS, 1973. “AQVAL/1—computer implementation of a variable-valued logic system and the application to pattern recognition” In: Proceedings 1st International Joint Conference on Pattern Recognition.Washington.
Google Scholar
|
|
Michalski RS, 1983. “A theory and methodology of inductive learning” In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An Artificial Intelligence Approach (Vol I)Tioga.
Google Scholar
|
|
Michalski RS and Chilauski RL, 1980. “Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soyabean disease diagnosis” International Journal of Policy Analysis and Information Systems4(2), 125–161.
Google Scholar
|
|
Michalski RS and Stepp RE, 1983. “Learning from observation: conceptual clustering” In: Michalski RS, Carbonell JG and Mitchell TM (eds.), Machine Learning: An Artificial Intelligence Approach (vol. I). Tioga.
Google Scholar
|
|
Michalski RS, Carbonell JG and Mitchell TM, 1983. Machine Learning: An Artificial Intelligence Approach (vol. I)Tioga.
Google Scholar
|
|
Michalski RS, Carbonell JG and Mitchell TM, 1986a. Machine Learning: An Artificial Intelligence Approach (vol. II)Morgan-Kaufman.
Google Scholar
|
|
Michalski RS, Mozetic I, Hong N and Lavrac N, 1986b. “The multi-purpose incremental learning system AQ15 and its testing application to three medical domains” In: Proceedings AAAI'86. Morgan-Kaufmann.
Google Scholar
|
|
Michie D, 1988. “Machine learning in the next five years” In: Proceedings EWSL'88. Pitman.
Google Scholar
|
|
Milosavljevic A, 1988. “Learning in the presence of background knowledge” Technical Report, Univ. Santa Cruz, California, UCSR-CRL–87–27.
Google Scholar
|
|
Mingers J, 1989a. “An empirical comparison of pruning methods for decision tree induction” Machine Learning3(3), 319–342.
Google Scholar
|
|
Mingers J, 1989b. “An empirical comparison of pruning methods for decision tree induction” Machine Learning4(2), 227–248.
Google Scholar
|
|
Mitchell TM, 1980. “The need for biases in learning generalisations” Technical Report CBM-TR–117, Department of Computer Science, Rutgers University.
Google Scholar
|
|
Mitchell TM, 1982. “Generalisation as search” In: Webber BL and Nilsson NJ (eds.), Readings in Artificial Intelligence. Troga Publishing Company.
Google Scholar
|
|
Mortimer H, 1988. The Logic of InductionEllis Horwood.
Google Scholar
|
|
Muggleton S, 1987. “Structuring knowledge by asking questions” In: Bratko I and Lavrac N (eds.), Progress in Machine Learning. Sigma Press, Wilmslow.
Google Scholar
|
|
Muggleton S, 1988. “A strategy for constructing new predicates in first order logic” In: Proceedings EWSL'88. Pitman.
Google Scholar
|
|
Muggleton S, 1991. “Inductive logic programming” New Generation Computing8(4), 295–318.
Google Scholar
|
|
Murray KS, 1987. “Multiple convergence: an approach to disjunctive concept acquisition” In: Proceedings 10th IJCAI'87. Morgan-Kaufmann.
Google Scholar
|
|
Neyman J, 1957. “Inductive behaviour as a basic concept of philosophy and science” Review of the International Statistical Institute 25.
Google Scholar
|
|
Nilsson N, 1965. Learning Machines: Foundations of Trainable Pattern Classifying Systems. McGraw-Hill.
Google Scholar
|
|
Pagallo G, 1989. “Learning DNF by decision trees” In: Proceedings 11th IJCAI-89. Morgan-Kaufmann.
Google Scholar
|
|
Pagallo G and Haussler D, 1988. “Feature discovery in empirical learning” University of California at Santa Cruz, Technical Report no. UCSC-CRL–88–08.
Google Scholar
|
|
Payne HJ and Meisel WS, 1977. “An algorithm for constructing optimal binary decision trees”. IEEE Transactions on ComputersC–26 (a) 09, 905–916.
Google Scholar
|
|
Pearl J, 1985. “Learning hidden causes from empirical data” In: Proceedings 9th IJCAI'85. Morgan-Kaufmann.
Google Scholar
|
|
Plotkin GD, 1971. “A further note on inductive generalisation” In: Machine Intelligence6. Meltzer B and Michie D (eds.). Elsevier, New York.
Google Scholar
|
|
Quinlan JR, 1986. “Induction of decision trees” In: Machine Learning7(1), 81–106.
Google Scholar
|
|
Quinlan JR, 1987a. “Generating production rules from decision trees” In: Proceedings 10th IJCAI'87. Morgan-Kaufmann.
Google Scholar
|
|
Quinlan JR, 1987b. “Simplifying decision trees” International journal of Man-Machine Studies27(3), 221–234.
Google Scholar
|
|
Quinlan JR, 1989a. “Unknown attribute values in induction” In: Proceedings 6th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Quinlan JR, 1989b. “Learning relations: comparison of a symbolic and a connectionist approach” Technical Report 346, University of Sydney.
Google Scholar
|
|
Quinlan JR, Compton PJ, Horn KA and Lazarus L, 1986. “Inductive knowledge acquisition: a case study” In: Proceedings Second Australian Conference on Applications of Expert Systems.Sydney.
Google Scholar
|
|
Rappaport AT and Gaines BR, 1990. “Integrated knowledge base building environments” Knowledge Acquisition2(1), 51–72.
Google Scholar
|
|
Reinke RE and Michalski RS, 1988. Incremental learning of concept descriptions: a method and experimental results” In: Hay JE and Michie D (eds.), Machine Intelligence11. Oxford Press.
Google Scholar
|
|
Rendell L, 1986. “A general framework for induction and a study of selective induction” In: Machine Learning1(2), 177–226.
Google Scholar
|
|
Rendell L, 1988. “Learning hard concepts through constructive induction: framework and rationale” Technical Report, Univ. of Illinois at Urbana Champaign, UIUCDS-R- 88–1426.
Google Scholar
|
|
Rendell L, Seshu R and Cheng D, 1987. “More robust concept learning using dynamically variable bias management” In: Proceedings 10th IJCAI'87. Morgan-Kaufmann.
Google Scholar
|
|
Schlimmer JC and Fisher D, 1986. “A case study of incremental concept induction” In: Proceedings AAAI'86. Morgan-Kaufmann.
Google Scholar
|
|
Schlimmer JC, 1987. “Incremental adjustment of representations for learning” In: Proceedings 4th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Sebestyen GS, 1962. “Pattern recognition by an adaptive process of sample set construction” IRE Transactions on Information TheoryIT–809, 82–91.
Google Scholar
|
|
Shapiro AD, 1987. Structured Induction For Expert SystemsTuring Institute Press, Addison-Wesley.
Google Scholar
|
|
Thornton C, 1987. “Hypercuboid-formation behaviour of two learning algorithms” In: Proceedings 10th IJCAl'87. Morgan-Kaufmann.
Google Scholar
|
|
Toussaint GT, 1974. “Bibliography on estimation of misclassification” IEEE Transactions on Information TheoryIT–20 (4) 07, 472–479.
Google Scholar
|
|
Utgoff PE, 1986. Machine Learning of Inductive BiasKluwer.
Google Scholar
|
|
Utgoff PE, 1988. “ID5: an incremental IDS” In: Proceedings 5th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|
|
Valiant L, 1984. “A theory of the learnable” Communications of the ACM27(11), 1134–1142.
Google Scholar
|
|
Vapnik VN and Chervonenkis AY, 1971. “On the uniform convergence of realative frequencies of events to their probabilities” Theory of Probability and its Applications16(2), 264–280.
Google Scholar
|
|
Vapnik VN, 1989. “Inductive principles for the search for empirical dependencies (methods based on weak convergence of probability measures)” In: Proceedings Second Annual Workshop on Computational Learning Theory. Santa Cruz. CA.Morgan-Kaufmann.
Google Scholar
|
|
Watanabe S, 1972. “Pattern recognition as information compression” In: Watanabe S (ed.), Frontiers of Pattern Recognition. Academic Press.
Google Scholar
|
|
Watanabe S, 1985. Pattern Recognition: Human and MechanicalWiley.
Google Scholar
|
|
Wilkins DC and Buchanan BG, 1986. “On debugging rule-sets when reasoning under uncertainty” In: Proceedings AAAI'86. Morgan-Kaufmann.
Google Scholar
|
|
Winston PH, 1975. The Psychology of Computer VisionMcGraw-Hill.
Google Scholar
|
|
Wirth J and Catlett J, 1988. “Experiments on the costs and benefits of windowing in ID3” In: Proceedings 5th International Workshop on Machine Learning. Morgan-Kaufmann.
Google Scholar
|