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)

60 ×#learning
17 ×#induction
12 ×#concept
10 ×#algorithm
8 ×#empirical
8 ×#incremental
8 ×#using
7 ×#bias
7 ×#problem
5 ×#framework

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.