## Alberto Maria Segre

*Proceedings of the Sixth International Workshop on Machine Learning*

ML, 1989.

@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.

60 ×#learning

17 ×#induction

12 ×#concept

10 ×#algorithm

8 ×#empirical

8 ×#incremental

8 ×#using

7 ×#bias

7 ×#problem

5 ×#framework

17 ×#induction

12 ×#concept

10 ×#algorithm

8 ×#empirical

8 ×#incremental

8 ×#using

7 ×#bias

7 ×#problem

5 ×#framework