Lawrence Birnbaum, Gregg Collins
Proceedings of the Eighth International Workshop on Machine Learning
ML, 1991.

@proceedings{ML-1991,
	address       = "Evanston, Illinois, USA",
	editor        = "Lawrence Birnbaum and Gregg Collins",
	isbn          = "1-55860-200-3",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the Eighth International Workshop on Machine Learning}",
	year          = 1991,
}

Contents (128 items)

48 ×#learning
18 ×#induction
12 ×#concept
8 ×#approach
7 ×#refinement
6 ×#incremental
6 ×#information retrieval
6 ×#modelling
5 ×#adaptation
5 ×#probability

ML-1991-GruberBBW #design #information management

Design Rationale Capture as Knowledge Acquisition (TRG, CB, JHB, JW), pp. 3–12.

ML-1991-Gil #effectiveness #framework #independence

A Domain-Independent Framework for Effective Experimentation in Planning (YG), pp. 13–17.

ML-1991-Jones #refinement #using

Knowledge Refinement Using a High Level, Non-Technical Vocabulary (EKJ), pp. 18–22.

ML-1991-MaW #consistency #knowledge base #optimisation #performance

Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method (YM, DCW), pp. 23–27.

ML-1991-CrawS #flexibility #refinement

The Flexibility of Speculative Refinement (SC, DHS), pp. 28–32.

ML-1991-WeintraubB #fault #generative #knowledge base

Generating Error Candidates for Assigning Blame in a Knowledge Base (MAW, TB), pp. 33–37.

ML-1991-Maza #concept #learning #prototype

A Prototype Based Symbolic Concept Learning System (MdlM), pp. 41–45.

ML-1991-FisherY #similarity

Combining Evidence of Deep and Surface Similarity (DHF, JPY), pp. 46–50.

ML-1991-GickM

The Importance of Causal Structure and Facts in Evaluating Explanations (MG, SM), pp. 51–54.

ML-1991-HastingsLL #learning #word

Learning Words From Context (PMH, SLL, RKL), pp. 55–59.

ML-1991-Iba #modelling

Modeling the Acquisition and Improvement of Motor Skkills (WI), pp. 60–64.

ML-1991-JonesV

A Computational Model of Acquisition for Children’s Addtion Strategies (RMJ, KV), pp. 65–69.

ML-1991-JordanR #learning #modelling

Internal World Models and Supervised Learning (MIJ, DER), pp. 70–74.

ML-1991-Kazman #named

Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition (RK), pp. 75–79.

ML-1991-LangleyA

The Acquisition of Human Planning Expertise (PL, JAA), pp. 80–84.

ML-1991-LevinsonS #adaptation

Adaptive Pattern-Oriented Chess (RL, RS), pp. 85–89.

ML-1991-MartinB #bias #learning #variability

Variability Bias and Category Learning (JDM, DB), pp. 90–94.

ML-1991-MillerL #constraints

A Constraint-Motivated Model of Lexical Acquisition (CSM, JEL), pp. 95–99.

ML-1991-NichollW #modelling #order

Computer Modelling of Acquisition Orders in Child Language (SN, DCW), pp. 100–104.

ML-1991-Shultz #development #modelling #simulation

Simulating Stages of Human Cognitive Development With Connectionist Models (TRS), pp. 105–109.

ML-1991-VanLehnJ #correctness #learning #physics

Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control (KV, RMJ), pp. 110–114.

ML-1991-Aha #approach #incremental #induction

Incremental Constructive Induction: An Instance-Based Approach (DWA), pp. 117–121.

ML-1991-CallanU #approach #induction

A Transformational Approach to Constructive Induction (JPC, PEU), pp. 122–126.

ML-1991-Day #csp #heuristic #learning #problem

Learning Variable Descriptors for Applying Heuristics Across CSP Problems (DSD), pp. 127–131.

ML-1991-Drastal #induction

Informed Pruning in Constructive Induction (GD), pp. 132–136.

ML-1991-FawcettU #generative #hybrid

A Hybrid Method for Feature Generation (TF, PEU), pp. 137–141.

ML-1991-GiordanaSR #concept

Abstracting Concepts with Inverse Resolution (AG, LS, DR), pp. 142–146.

ML-1991-GunschR #induction

Opportunistic Constructive Induction (GHG, LAR), pp. 147–152.

ML-1991-Kadie #induction #learning

Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning (CMK), pp. 153–157.

ML-1991-KowalczykFG #higher-order #network

Discovering Production Rules with Higher Order Neural Networks (AK, HLF, KG), pp. 158–162.

ML-1991-LengB #induction

Constructive Induction on Symbolic Features (BL, BGB), pp. 163–167.

ML-1991-LingN #comparison

Comparison of Methods Based on Inverse Resolution (XL, MAN), pp. 168–172.

ML-1991-Matheus #induction

The Need for Constructive Induction (CJM), pp. 173–177.

ML-1991-MooneyO #induction #refinement

Constructive Induction in Theory Refinement (RJM, DO), pp. 178–182.

ML-1991-MurphyP #induction

Constructive Induction of M-of-N Terms (PMM, MJP), pp. 183–187.

ML-1991-RagavanR #empirical #learning

Relations, Knowledge and Empirical Learning (HR, LAR), pp. 188–192.

ML-1991-OliveiraS #concept #learning #network

Learning Concepts by Synthesizing Minimal Threshold Gate Networks (ALO, ALSV), pp. 193–197.

ML-1991-Saxena #on the #representation

On the Effect of Instance Representation on Generalization (SS), pp. 198–202.

ML-1991-SilversteinP #induction #learning #relational

Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.

ML-1991-SuttonM #learning #polynomial

Learning Polynomial Functions by Feature Construction (RSS, CJM), pp. 208–212.

ML-1991-TowellCS #induction #knowledge-based #network

Constructive Induction in Knowledge-Based Neural Networks (GGT, MC, JWS), pp. 213–217.

ML-1991-WatanabeR

Feature Construction in Structural Decision Trees (LW, LAR), pp. 218–222.

ML-1991-YangRB #case study #comparative

Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme (DSY, LAR, GB), pp. 223–227.

ML-1991-Yeung #approach #induction #network

A Neural Network Approach to Constructive Induction (DYY), pp. 228–232.

ML-1991-Lewis #information retrieval #learning

Learning in Intelligent Information Retrieval (DDL), pp. 235–239.

ML-1991-BhuyanR #adaptation #clustering #information retrieval #probability

A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information Retrieval (JNB, VVR), pp. 240–244.

ML-1991-CrawfordFAT #classification #information retrieval

Classification Trees for Information Retrieval (SLC, RMF, LAA, RMT), pp. 245–249.

ML-1991-BhatiaDR #information management #query

Query Formulation Through Knowledge Acquisition (SKB, JSD, VVR), pp. 250–254.

ML-1991-GokerM #incremental #information retrieval #learning

Incremental Learning in a Probalistic Information Retrieval System (AG, TLM), pp. 255–259.

ML-1991-Kwok #adaptation #architecture #learning #query #using

Query Learning Using an ANN with Adaptive Architecture (KLK), pp. 260–264.

ML-1991-RamH #approach #information retrieval

A Goal-Based Approach to Intelligent Information Retrieval (AR, LH), pp. 265–269.

ML-1991-Thompson #approach #information retrieval #machine learning

Machine Learning in the Combination of Expert Opinion Approach to IR (PT), pp. 270–274.

ML-1991-Walczak #induction #performance #predict

Predicting Actions from Induction on Past Performance (SW), pp. 275–279.

ML-1991-Brand #learning

Decision-Theoretic Learning in an Action System (MB), pp. 283–287.

ML-1991-ChienGD #learning #on the

On Becoming Decreasingly Reactive: Learning to Deliberate Minimally (SAC, MTG, GD), pp. 288–292.

ML-1991-CobbG #learning #persistent

Learning the Persistence of Actions in Reactive Control Rules (HGC, JJG), pp. 292–297.

ML-1991-MillanT #learning

Learning to Avoid Obstacles Through Reinforcement (JdRM, CT), pp. 298–302.

ML-1991-HsuS #evaluation #learning

Learning Football Evaluation for a Walking Robot (GTH, RGS), pp. 303–307.

ML-1991-KedarBD #approximate #refinement

The Blind Leading the Blind: Mutual Refinement of Approximate Theories (SK, JLB, CLD), pp. 308–312.

ML-1991-KokarR #learning

Learning to Select a Model in a Changing World (MMK, SAR), pp. 313–317.

ML-1991-Krulwich #learning

Learning from Deliberated Reactivity (BK), pp. 318–322.

ML-1991-Lin #education #learning #self

Self-improvement Based on Reinforcement Learning, Planning and Teaching (LJL), pp. 323–327.

ML-1991-MahadevanC #architecture #learning #scalability

Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture (SM, JC), pp. 328–332.

ML-1991-Moore #programming

Variable Resolution Dynamic Programming (AWM), pp. 333–337.

ML-1991-Pierce #learning #set

Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus (DRP), pp. 338–342.

ML-1991-Ring #behaviour #development #incremental

Incremental Development of Complex Behaviors (MBR), pp. 343–347.

ML-1991-Singh #composition #learning

Transfer of Learning Across Compositions of Sequentail Tasks (SPS), pp. 348–352.

ML-1991-Sutton #incremental #programming

Planning by Incremental Dynamic Programming (RSS), pp. 353–357.

ML-1991-Tan #learning #representation

Learning a Cost-Sensitive Internal Representation for Reinforcement Learning (MT), pp. 358–362.

ML-1991-Whitehead #complexity

Complexity and Cooperation in Q-Learning (SDW), pp. 363–367.

ML-1991-AllenT #concept #probability #relational

Probabilistic Concept Formation in Relational Domains (JAA, KT), pp. 375–379.

ML-1991-Bain #learning

Experiments in Non-Monotonic Learning (MB), pp. 380–384.

ML-1991-BratkoMV #learning #modelling

Learning Qualitative Models of Dynamic Systems (IB, SM, AV), pp. 385–388.

ML-1991-BrunkP #algorithm #concept #learning #relational

An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.

ML-1991-RaedtBM #concept #constraints #interactive

Integrity Constraints and Interactive Concept-Learning (LDR, MB, BM), pp. 394–398.

ML-1991-DzeroskiL #comparison #empirical #learning

Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL (SD, NL), pp. 399–402.

ML-1991-Feng #fault

Inducing Temporal Fault Diagnostic Rules from a Qualitative Model (CF), pp. 403–406.

ML-1991-HirakiGYA #image #learning

Learning Spatial Relations from Images (KH, JHG, YY, YA), pp. 407–411.

ML-1991-HummeS #using

Using Inverse Resolution to Learn Relations from Experiments (DH, CS), pp. 412–416.

ML-1991-KijsirikulNS #learning #logic programming #performance #source code

Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals (BK, MN, MS), pp. 417–421.

ML-1991-LeckieZ #approach #induction #learning

Learning Search Control Rules for Planning: An Inductive Approach (CL, IZ), pp. 422–426.

ML-1991-PageF #learning

Learning Constrained Atoms (CDPJ, AMF), pp. 427–431.

ML-1991-PazzaniBS #approach #concept #learning #relational

A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.

ML-1991-QianI #axiom #concept #consistency

The Consistent Concept Axiom (ZQ, KBI), pp. 437–441.

ML-1991-Quinlan #induction #logic programming

Determinate Literals in Inductive Logic Programming (JRQ), pp. 442–446.

ML-1991-RichardsM #first-order

First-Order Theory Revision (BLR, RJM), pp. 447–451.

ML-1991-Rouveirol #induction

Completeness for Inductive Procedures (CR), pp. 452–456.

ML-1991-WirthO #constraints

Constraints on Predicate Invention (RW, PO), pp. 457–461.

ML-1991-Wogulis #relational

Revising Relational Domain Theories (JW), pp. 462–466.

ML-1991-YamanishiK #learning #probability #search-based #sequence

Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.

ML-1991-Berenji #approximate #learning #refinement

Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning (HRB), pp. 475–479.

ML-1991-BottaRSS #abduction #learning #using

Improving Learning Using Causality and Abduction (MB, SR, LS, SBS), pp. 480–484.

ML-1991-Cain

The DUCTOR: A Theory Revision System for Propositional Domains (TC), pp. 485–489.

ML-1991-Cohen

The Generality of Overgenerality (WWC), pp. 490–494.

ML-1991-desJardins #bias #learning #probability

Probabilistic Evaluating of Bias for Learning Systems (Md), pp. 495–499.

ML-1991-FeldmanSK #approximate #incremental #refinement

Incremental Refinement of Approximate Domain Theories (RF, AMS, MK), pp. 500–504.

ML-1991-Gordon

An Enhancer for Reactive Plans (DFG), pp. 505–508.

ML-1991-GratchD #approach #effectiveness #hybrid

A Hybrid Approach to Guaranteed Effective Control Strategies (JG, GD), pp. 509–513.

ML-1991-Hamakawa #refinement

Revision Cost for Theory Refinement (RH), pp. 514–518.

ML-1991-LingV

Revision of Reduced Theories (XL, MV), pp. 519–523.

ML-1991-MaclinS #automaton #finite

Refining Domain Theories Expressed as Finite-State Automata (RM, JWS), pp. 524–528.

ML-1991-Nedellec

A Smallest Generalization Step Strategy (CN), pp. 529–533.

ML-1991-OurstonM #multi

Improving Shared Rules in Multiple Category Domain Theories (DO, RJM), pp. 534–538.

ML-1991-Shen #knowledge base #scalability

Discovering Regularities from Large Knowledge Bases (WMS), pp. 539–543.

ML-1991-Tadepalli #learning

Learning with Incrutable Theories (PT), pp. 544–548.

ML-1991-TecuciM #adaptation #learning #multi

A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications (GT, RSM), pp. 549–553.

ML-1991-ThompsonLI #concept #using

Using Background Knowledge in Concept Formation (KT, PL, WI), pp. 554–558.

ML-1991-WhitehallL #case study #how #knowledge-based #learning

A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems (BLW, SCYL), pp. 559–563.

ML-1991-WisniewskiM #data-driven

Is it a Pocket or a Purse? Tighly Coupled Theory and Data Driven Learing (EJW, DLM), pp. 564–568.

ML-1991-YooF #bound #effectiveness #identification

Identifying Cost Effective Boundaries of Operationality (JPY, DHF), pp. 569–573.

ML-1991-ChienWDDFGL #automation #machine learning

Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.

ML-1991-BelyaevF #classification

Noise-Resistant Classification (LVB, LPF), pp. 581–585.

ML-1991-BennettD #probability

Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans (SB, GD), pp. 586–590.

ML-1991-BiswasWYK #clustering #concept #data analysis

Conceptual Clustering and Exploratory Data Analysis (GB, JBW, QY, GRK), pp. 591–595.

ML-1991-Catlett #named

Megainduction: A Test Flight (JC), pp. 596–599.

ML-1991-CerboneD #compilation #optimisation

Knowledge Compilation to Speed Up Numerical Optimization (GC, TGD), pp. 600–604.

ML-1991-Goel #formal method #incremental #learning

Model Revision: A Theory of Incremental Model Learning (AKG), pp. 605–609.

ML-1991-Herrmann #learning

Learning Analytical Knowledge About VLSI-Design from Observation (JH), pp. 610–614.

ML-1991-Kadie91a #concept #learning #set

Continous Conceptual Set Covering: Learning Robot Operators From Examples (CMK), pp. 615–619.

ML-1991-ORorkeMABC #evaluation #machine learning

Machine Learning for Nondestructive Evaluation (PO, SM, MA, WB, DCSC), pp. 620–624.

ML-1991-PachowiczB #concept #effectiveness #recognition

Improving Recognition Effectiveness of Noisy Texture Concepts (PP, JWB), pp. 625–629.

ML-1991-RaoLS #equation #knowledge-based

Knowledge-Based Equation Discovery in Engineering Domains (RBR, SCYL, RES), pp. 630–634.

ML-1991-Reich #design #learning

Design Integrated Learning Systems for Engineering Design (YR), pp. 635–639.

ML-1991-Schlimmer #consistency #database #induction #learning

Database Consistency via Inductive Learning (JCS), pp. 640–644.

ML-1991-TchengLLR #adaptation #interactive #modelling #named

AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making (DKT, BLL, SCYL, LAR), pp. 645–649.

ML-1991-WatanabeY #3d #induction

Decision Tree Induction of 3-D Manufacturing Features (LW, SY), pp. 650–654.

ML-1991-MartinSC #information management

Knowledge Acquisition Combining Analytical and Empirrcal Techniques (MM, RS, UC), pp. 657–661.

ML-1991-Wixson #composition #learning #scalability

Scaling Reinforcement Learning Techniques via Modularity (LEW), pp. 3368–372.