Proceedings of the Sixth International Workshop on Machine Learning
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Alberto Maria Segre
Proceedings of the Sixth International Workshop on Machine Learning
ML, 1989.

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@proceedings{ML-1989,
	address       = "Ithaca, New York, USA",
	editor        = "Alberto Maria Segre",
	isbn          = "1-55860-036-1",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the Sixth International Workshop on Machine Learning}",
	year          = 1989,
}

Contents (128 items)

ML-1989-Langley #empirical #learning
Unifying Themes in Empirical and Explanation-Based Learning (PL), pp. 2–4.
ML-1989-MooneyO #aspect-oriented #concept #induction #learning
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects (RJM, DO), pp. 5–7.
ML-1989-YooF #clustering #concept
Conceptual Clustering of Explanations (JPY, DHF), pp. 8–10.
ML-1989-Widmer #deduction #integration #learning
A Tight Integration of Deductive Learning (GW), pp. 11–13.
ML-1989-TecuciK #learning #multi
Multi-Strategy Learning in Nonhomongeneous Domain Theories (GT, YK), pp. 14–16.
ML-1989-ZhangM #learning
A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues (JZ, RSM), pp. 17–19.
ML-1989-Redmond #learning #reasoning
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction (MR), pp. 20–22.
ML-1989-BergadanoGP #deduction #induction #learning #top-down
Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.
ML-1989-SarrettP #algorithm #empirical #learning
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.
ML-1989-Hirsh #empirical #learning
Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
ML-1989-Danyluk #bias #induction #information management
Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information (APD), pp. 34–36.
ML-1989-Fawcett #learning
Learning from Plausible Explanations (TF), pp. 37–39.
ML-1989-Ali
Augmenting Domain Theory for Explanation-Based Generalization (KMA), pp. 40–42.
ML-1989-Haines #learning
Explanation Based Learning as Constrained Search (DH), pp. 43–45.
ML-1989-Morris #learning
Reducing Search and Learning Goal Preferences (SM), pp. 46–48.
ML-1989-Kass #adaptation
Adaptation-Based Explanation: Explanations as Cases (AK), pp. 49–51.
ML-1989-Seifert #feature model #retrieval #using
A Retrieval Model Using Feature Selection (CMS), pp. 52–54.
ML-1989-KrulwichCB #experience
Improving Decision-Making on the Basis of Experience (BK, GC, LB), pp. 55–57.
ML-1989-NumaoS #learning #similarity
Explanation-Based Acceleration of Similarity-Based Learning (MN, MS), pp. 58–60.
ML-1989-Hunter #information management
Knowledge Acquisition Planning: Results and Prospects (LH), pp. 61–65.
ML-1989-Diederich #learning
“Learning by Instruction” in connectionist Systems (JD), pp. 66–68.
ML-1989-Katz #learning #network
Integrating Learning in a Neural Network (BFK), pp. 69–71.
ML-1989-Pazzani #learning
Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.
ML-1989-FriedrichN #algorithm #induction #learning #using
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis (GF, WN), pp. 75–77.
ML-1989-Wogulis #framework #performance
A Framework for Improving Efficiency and Accuracy (JW), pp. 78–80.
ML-1989-DrastalMR #fault #induction
Error Correction in Constructive Induction (GD, RM, SR), pp. 81–83.
ML-1989-BarlettaK #empirical #learning
Improving Explanation-Based Indexing with Empirical Learning (RB, RK), pp. 84–86.
ML-1989-Wollowski #learning
A Schema for an Integrated Learning System (MW), pp. 87–89.
ML-1989-ShavlikT #learning #network
Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.
ML-1989-Buntine #classification #learning #using
Learning Classification Rules Using Bayes (WLB), pp. 94–98.
ML-1989-GamsK #empirical #learning
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains (MG, AK), pp. 99–103.
ML-1989-Chan #induction #learning
Inductive Learning with BCT (PKC), pp. 104–108.
ML-1989-RuffD #question #what
What Good Are Experiments? (RAR, TGD), pp. 109–112.
ML-1989-MuggletonBMM #comparison #machine learning
An Experimental Comparison of Human and Machine Learning Formalisms (SM, MB, JHM, DM), pp. 113–118.
ML-1989-PagalloH #algorithm
Two Algorithms That Learn DNF by Discovering Relevant Features (GP, DH), pp. 119–123.
ML-1989-Dietterich #induction #learning
Limitations on Inductive Learning (TGD), pp. 124–128.
ML-1989-GoodmanS #algorithm #induction #probability #set
The Induction of Probabilistic Rule Sets — The Itrule Algorithm (RMG, PS), pp. 129–132.
ML-1989-Holder #empirical
Empirical Substructure Discovery (LBH), pp. 133–136.
ML-1989-Paredis #behaviour #learning
Learning the Behavior of Dynamical Systems form Examples (JP), pp. 137–140.
ML-1989-MasonCM #learning
Experiments in Robot Learning (MTM, ADC, TMM), pp. 141–145.
ML-1989-SpanglerFU #induction
Induction of Decision Trees from Inconclusive Data (WSS, UMF, RU), pp. 146–150.
ML-1989-Manago #induction
Knowledge Intensive Induction (MM), pp. 151–155.
ML-1989-Gaines #data-driven #database #empirical #induction #statistics #trade-off
An Ounce of Knowledge is Worth a Ton of Data: Quantitative studies of the Trade-Off between Expertise and Data Based On Statistically Well-Founded Empirical Induction (BRG), pp. 156–159.
ML-1989-Spackman #detection #induction #learning #tool support
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning (KAS), pp. 160–163.
ML-1989-Quinlan #induction
Unknown Attribute Values in Induction (JRQ), pp. 164–168.
ML-1989-FisherMMST #learning
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
ML-1989-Schaffer #data analysis
Bacon, Data Analysis and Artificial Intelligence (CS), pp. 174–179.
ML-1989-RudyK #learning
Learning to Plan in Complex Domains (DR, DFK), pp. 180–182.
ML-1989-Shavlik #analysis #empirical #learning
An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
ML-1989-HilliardLRP #approach #classification #hybrid #learning #problem #scheduling
Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach (MRH, GEL, GR, MRP), pp. 188–190.
ML-1989-LeviPS #learning
Learning Tactical Plans for Pilot Aiding (KRL, DLP, VLS), pp. 191–193.
ML-1989-BirnbaumCK
Issues in the Justification-Based Diagnosis of Planning Failures (LB, GC, BK), pp. 194–196.
ML-1989-MatwinM #learning
Learning Procedural Knowledge in the EBG Context (SM, JM), pp. 197–199.
ML-1989-Puget #invariant #learning
Learning Invariants from Explanations (JFP), pp. 200–204.
ML-1989-SobekL #learning #using
Using Learning to Recover Side-Effects of Operators in Robotics (RPS, JPL), pp. 205–208.
ML-1989-ORorkeCO #learning
Learning to Recognize Plans Involving Affect (PO, TC, AO), pp. 209–211.
ML-1989-Jones #learning #problem
Learning to Retrieve Useful Information for Problem Solving (RMJ), pp. 212–214.
ML-1989-VanLehn #problem #what
Discovering Problem Solving Strategies: What Humans Do and Machines Don’t (Yet) (KV), pp. 215–217.
ML-1989-ChaseZPBMH #approximate
Approximating Learned Search Control Knowledge (MPC, MZ, RLP, JDB, PPM, HH), pp. 218–220.
ML-1989-Tadepalli #approximate
Planning Approximate Plans for Use in the Real World (PT), pp. 224–228.
ML-1989-AllenL #concept #using
Using Concept Hierarchies to Organize Plan Knowledge (JAA, PL), pp. 229–231.
ML-1989-YangF #clustering #concept
Conceptual Clustering of Mean-Ends Plans (HY, DHF), pp. 232–234.
ML-1989-Flann #abstraction #learning #problem
Learning Appropriate Abstractions for Planning in Formation Problems (NSF), pp. 235–239.
ML-1989-MostowP #heuristic #optimisation
Discovering Admissible Search Heuristics by Abstracting and Optimizing (JM, AP), p. 240.
ML-1989-Knoblock #abstraction #learning
Learning Hierarchies of Abstraction Spaces (CAK), pp. 241–245.
ML-1989-ConverseHM #learning
Learning from Opportunity (TMC, KJH, MM), pp. 246–248.
ML-1989-Chien #learning
Learning by Analyzing Fortuitous Occurrences (SAC), pp. 249–251.
ML-1989-GervasioD #learning
Explanation-Based Learning of Reactive Operations (MTG, GD), pp. 252–254.
ML-1989-BlytheM #on the
On Becoming Reactive (JB, TMM), pp. 255–259.
ML-1989-Ginsberg #knowledge base #refinement
Knowledge Base Refinement and Theory Revision (AG), pp. 260–265.
ML-1989-ORorkeMS #abduction #case study
Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution (PO, SM, DS), pp. 266–271.
ML-1989-Rose #using
Using Domain Knowledge to Aid Scientific Theory Revision (DR), pp. 272–277.
ML-1989-KulkarniS
The Role of Experimentation in Scientific Theory Revision (DK, HAS), pp. 278–283.
ML-1989-Rajamoney #approach #consistency #experience #problem
Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem (SAR), pp. 284–289.
ML-1989-MurrayP #integration
Controlling Search for the Consequences of New Information During Knowledge Integration (KSM, BWP), pp. 290–295.
ML-1989-LeviSP #behaviour #identification #knowledge base
Identifying Knowledge Base Deficiencies by Observing User Behavior (KRL, VLS, DLP), pp. 296–301.
ML-1989-TongF #automation #case study #re-engineering #towards
Toward Automated Rational Reconstruction: A Case Study (CT, PF), pp. 302–307.
ML-1989-SimsB
Discovering Mathematical Operation Definitions (MHS, JLB), pp. 308–313.
ML-1989-RasZ #concept #learning
Imprecise Concept Learning within a Growing Language (ZWR, MZ), pp. 314–319.
ML-1989-Mahadevan #problem #using
Using Determinations in EBL: A Solution to the incomplete Theory Problem (SM), pp. 320–325.
ML-1989-Valtorta #complexity #knowledge-based #refinement
Some Results on the Complexity of Knowledge-Based Refinement (MV), pp. 326–331.
ML-1989-WilkinsT #consistency #knowledge base #refinement
Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory (DCW, KWT), pp. 332–339.
ML-1989-Grefenstette #algorithm #incremental #learning #search-based
Incremental Learning of Control Strategies with Genetic algorithms (JJG), pp. 340–344.
ML-1989-Anderson #learning #network
Tower of Hanoi with Connectionist Networks: Learning New Features (CWA), pp. 345–349.
ML-1989-Kaelbling #embedded #framework #learning
A Formal Framework for Learning in Embedded Systems (LPK), pp. 350–353.
ML-1989-WhiteheadB
A Role for Anticipation in Reactive Systems that Learn (SDW, DHB), pp. 354–357.
ML-1989-ScottM #case study #experience #learning #nondeterminism
Uncertainty Based Selection of Learning Experiences (PDS, SM), pp. 358–361.
ML-1989-Utgoff #incremental #learning
Improved Training Via Incremental Learning (PEU), pp. 362–365.
ML-1989-ClearwaterCHB #incremental #learning
Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
ML-1989-ThompsonL #concept #incremental
Incremental Concept Formation with Composite Objects (KT, PL), pp. 371–374.
ML-1989-CaruanaSE #algorithm #bias #induction #multi #search-based #using
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms (RC, JDS, LJE), pp. 375–378.
ML-1989-Gennari #concept
Focused Concept Formation (JHG), pp. 379–382.
ML-1989-Cornuejols #incremental #learning
An Exploration Into Incremental Learning: the INFLUENCE System (AC), pp. 383–386.
ML-1989-Aha #concept #incremental #independence #learning
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions (DWA), pp. 387–391.
ML-1989-TanS #approach #concept #learning #recognition
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition (MT, JCS), pp. 392–395.
ML-1989-Martin #learning
Reducing Redundant Learning (JDM), pp. 396–399.
ML-1989-Segen #clustering #incremental #representation
Incremental Clustering by Minimizing Representation Length (JS), pp. 400–403.
ML-1989-MarkovitchS #implementation
Information Filters and Their Implementation in the SYLLOG System (SM, PDS), pp. 404–407.
ML-1989-WefaldR #adaptation #learning
Adaptive Learning of Decision-Theoretic Search Control Knowledge (EW, SJR), pp. 408–411.
ML-1989-Selfridge #adaptation #case study #contest #learning
Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition (OGS), pp. 412–415.
ML-1989-Fogarty #algorithm #incremental #learning #realtime #search-based
An Incremental Genetic Algorithm for Real-Time Learning (TCF), pp. 416–419.
ML-1989-YagerF #learning
Participatory Learning: A Constructivist Model (RRY, KMF), pp. 420–425.
ML-1989-Subramanian #machine learning
Representational Issues in Machine Learning (DS), pp. 426–429.
ML-1989-Woodfill
Labor Saving New Distinctions (JW), pp. 430–433.
ML-1989-Subramanian89a #formal method
A Theory of Justified Reformulations (DS), pp. 434–438.
ML-1989-Riddle #reduction
Reformation from State Space to Reduction Space (PJR), pp. 439–440.
ML-1989-Callan #generative #knowledge-based
Knowledge-Based Feature Generation (JPC), pp. 441–443.
ML-1989-MaclinS
Enriching Vocabularies by Generalizing Explanation Structures (RM, JWS), pp. 444–446.
ML-1989-DietzenP #framework #higher-order #logic
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization (SD, FP), pp. 447–449.
ML-1989-Greiner #analysis #formal method #towards
Towards a Formal Analysis of EBL (RG), pp. 450–453.
ML-1989-HolteZ #framework #representation
A Mathematical Framework for Studying Representation (RCH, RMZ), pp. 454–456.
ML-1989-Schlimmer #problem #quality
Refining Representations to Improve Problem Solving Quality (JCS), pp. 457–460.
ML-1989-Rendell #induction
Comparing Systems and analyzing Functions to Improve Constructive Induction (LAR), pp. 461–464.
ML-1989-Saxena
Evaluating alternative Instance Representations (SS), pp. 465–468.
ML-1989-Chrisman #bias
Evaluating Bias During Pac-Learning (LC), pp. 469–471.
ML-1989-Mehra #framework #induction
Constructive Induction Framework (PM), pp. 474–475.
ML-1989-RaedtB #induction
Constructive Induction by Analogy (LDR, MB), pp. 476–477.
ML-1989-Kokar #concept #embedded
Concept Discovery Through Utilization of Invariance Embedded in the Description Language (MMK), pp. 478–479.
ML-1989-GrosofR #bias #declarative
Declarative Bias for Structural Domains (BNG, SJR), pp. 480–482.
ML-1989-Keller #compilation #learning #performance
Compiling Learning Vocabulary from a Performance System Description (RMK), pp. 482–495.
ML-1989-MohanT #algorithm #automation
Automatic Construction of a Hierarchical Generate-and-Test Algorithm (SM, CT), pp. 483–484.
ML-1989-Hsu #analysis
A Knowledge-Level Analysis of Informing (JYjH), pp. 485–488.
ML-1989-Mostow #algorithm #object-oriented #representation
An Object-Oriented Representation for Search algorithms (JM), pp. 489–491.
ML-1989-LambertTL #algorithm #concept #hybrid #learning #recursion
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts (BLL, DKT, SCYL), pp. 496–498.
ML-1989-Gordon #bias
Screening Hypotheses with Explicit Bias (DFG), pp. 499–500.
ML-1989-Marie #bias #dependence #learning
Building A Learning Bias from Perceived Dependencies (CdSM), pp. 501–502.
ML-1989-MorkK #approach #clustering #concept
A Bootstrapping Approach to Concept Clustering (KM, JUK), pp. 503–504.
ML-1989-Tallis #bias
Overcoming Feature Space Bias in a Reactive Environment (HT), pp. 505–508.

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