Derek H. Sleeman, Peter Edwards
Proceedings of the Ninth International Workshop on Machine Learning
ML, 1992.

@proceedings{ML-1992,
	address       = "Aberdeen, Scotland, United Kingdom",
	editor        = "Derek H. Sleeman and Peter Edwards",
	isbn          = "1-55860-247-X",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the Ninth International Workshop on Machine Learning}",
	year          = 1992,
}

Contents (60 items)

24 ×#learning
7 ×#concept
7 ×#induction
6 ×#named
5 ×#algorithm
5 ×#approach
4 ×#multi
4 ×#performance
4 ×#problem
4 ×#using

ML-1992-Aha #case study

Generalizing from Case studies: A Case Study (DWA), pp. 1–10.

ML-1992-AlmuallimD #concept #learning #on the

On Learning More Concepts (HA, TGD), pp. 11–19.

ML-1992-BalaMW #induction

The Principal Axes Method for Constructive Induction (JWB, RSM, JW), pp. 20–29.

ML-1992-Bhatnagar #learning

Learning by Incomplete Explanation-Based Learning (NB), pp. 37–42.

ML-1992-Carpineto #consistency #induction #performance

Trading Off Consistency and Efficiency in version-Space Induction (CC), pp. 43–48.

ML-1992-Catlett #named

Peepholing: Choosing Attributes Efficiently for Megainduction (JC), pp. 49–54.

ML-1992-Chen #learning

Improving Path Planning with Learning (PCC), pp. 55–61.

ML-1992-ChengS #representation

The Right Representation for Discovery: Finding the Conservation of Momentum (PCHC, HAS), pp. 62–71.

ML-1992-Christiansen #learning #nondeterminism #predict

Learning to Predict in Uncertain Continuous Tasks (ADC), pp. 72–81.

ML-1992-ClarkH #integration #lazy evaluation #partial evaluation

Lazy Partial Evaluation: An Integration of Explanation-Based Generalization and Partial Evaluation (PC, RCH), pp. 82–91.

ML-1992-ClouseU #education #learning

A Teaching Method for Reinforcement Learning (JAC, PEU), pp. 92–110.

ML-1992-ConklinG

Spatial Analogy and Subsumption (DC, JIG), pp. 111–116.

ML-1992-ConverseH #learning

Learning to Satisfy Conjunctive Goals (TMC, KJH), pp. 117–122.

ML-1992-CoxR #learning #multi

Multistrategy Learning with Introspective Meta-Explanations (MTC, AR), pp. 123–128.

ML-1992-Etzioni #analysis #learning

An Asymptotic Analysis of Speedup Learning (OE), pp. 129–136.

ML-1992-EtzioniM #why

Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches (OE, SM), pp. 137–143.

ML-1992-FawcettU #automation #generative #problem

Automatic Feature Generation for Problem Solving Systems (TF, PEU), pp. 144–153.

ML-1992-FengM #higher-order #induction #logic #towards

Towards Inductive Generalization in Higher Order Logic (CF, SM), pp. 154–162.

ML-1992-FisherXZ #clustering

Ordering Effects in Clustering (DHF, LX, NZ), pp. 162–168.

ML-1992-GiordanaS #algorithm #concept #learning #search-based #using

Learning Structured Concepts Using Genetic Algorithms (AG, CS), pp. 169–178.

ML-1992-GratchD #analysis #learning #problem

An Analysis of Learning to Plan as a Search Problem (JG, GD), pp. 179–188.

ML-1992-GrefenstetteR #approach #learning

An Approach to Anytime Learning (JJG, CLR), pp. 189–195.

ML-1992-Hickey #algorithm #approach #evaluation #towards

Artificial Universes — Towards a Systematic Approach to Evaluation Algorithms which Learn form Examples (RJH), pp. 196–205.

ML-1992-HirschbergP #analysis #concept #learning

Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.

ML-1992-HoggerB #approach #heuristic #learning #logic programming #source code

The MENTLE Approach to Learning Heuristics for the Control of Logic Programs (EIH, KB), pp. 212–217.

ML-1992-HolderCB #fuzzy

Fuzzy Substructure Discovery (LBH, DJC, HB), pp. 218–223.

ML-1992-HunterHS #classification #performance

Efficient Classification of Massive, Unsegmented Datastreams (LH, NLH, DJS), pp. 224–232.

ML-1992-IbaL #induction

Induction of One-Level Decision Trees (WI, PL), pp. 233–240.

ML-1992-Janikow #contest #induction #learning

Combining Competition and Cooperation in Supervised Inductive Learning (CZJ), pp. 241–248.

ML-1992-KiraR #approach #feature model

A Practical Approach to Feature Selection (KK, LAR), pp. 249–256.

ML-1992-KononenkoK #generative #learning #multi #optimisation #probability

Learning as Optimization: Stochastic Generation of Multiple Knowledge (IK, MK), pp. 257–262.

ML-1992-Laird #optimisation

Dynamic Optimization (PL), pp. 263–272.

ML-1992-LapointeM #induction #named #performance #recursion #source code

Sub-unification: A Tool for Efficient Induction of Recursive Programs (SL, SM), pp. 273–281.

ML-1992-LiuS #natural language

Augmenting and Efficiently Utilizing Domain Theory in Explanation-Based Natural Language Acquisition (RLL, VWS), pp. 282–289.

ML-1992-Mahadevan #learning #modelling #probability

Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions (SM), pp. 290–299.

ML-1992-Mao #learning #named

THOUGHT: An Integrated Learning System for Acquiring Knowledge Structure (CM), pp. 300–309.

ML-1992-Markov #approach #concept #learning

An Approach to Concept Learning Based on Term Generalization (ZM), pp. 310–315.

ML-1992-McCallum #learning #performance #proximity #using

Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.

ML-1992-Merckt #concept #flexibility #named

NFDT: A System that Learns Flexible Concepts Based on Decision Trees for Numerical Attributes (TVdM), pp. 322–331.

ML-1992-Moulet #algorithm

A Symbolic Algorithm for Computing Coefficients’ Accuracy in Regression (MM), pp. 332–337.

ML-1992-MuggletonSB

Compression, Significance, and Accuracy (SM, AS, MB), pp. 338–347.

ML-1992-Niquil #generative

Guiding Example Acquisition by Generating Scenarios (YN), pp. 348–354.

ML-1992-OliveiraS #feature model #induction #using

Constructive Induction Using a Non-Greedy Strategy for Feature Selection (ALO, ALSV), pp. 355–360.

ML-1992-OmlinG #higher-order #network #using

Training Second-Order Recurrent Neural Networks using Hints (CWO, CLG), pp. 361–366.

ML-1992-PerezE #named #problem

DYNAMIC: A New Role for Training Problems in EBL (MAP, OE), pp. 367–372.

ML-1992-RadiyaZ #framework #modelling

A Framework for Discovering Discrete Event Models (AR, JMZ), pp. 373–378.

ML-1992-RubyK #learning #optimisation

Learning Episodes for Optimization (DR, DFK), pp. 379–384.

ML-1992-SammutHKM #learning

Learning to Fly (CS, SH, DK, DM), pp. 385–393.

ML-1992-Schaffer #problem #recognition

Deconstructing the Digit Recognition Problem (CS), pp. 394–399.

ML-1992-Segre #multi #on the

On Combining Multiple Speedup Techniques (AMS), pp. 400–405.

ML-1992-Singh #algorithm #learning #modelling #scalability

Scaling Reinforcement Learning Algorithms by Learning Variable Temporal Resolution Models (SPS), pp. 406–415.

ML-1992-SmythM #detection #fault #novel

Detecting Novel Classes with Applications to Fault Diagnosis (PS, JM), pp. 416–425.

ML-1992-SubramanianH #algorithm #design

Measuring Utility and the Design of Provably Good EBL Algorithms (DS, SH), pp. 426–425.

ML-1992-TangkitvanichS #concept #fault #multi #relational

Refining a Relational Theory with Multiple Faults in the Concept and Subconcepts (ST, MS), pp. 436–444.

ML-1992-Tecuci #knowledge base #refinement

Cooperation in Knowledge Base Refinement (GT), pp. 445–450.

ML-1992-Tesauro #difference #learning

Temporal Difference Learning of Backgammon Strategy (GT), pp. 451–457.

ML-1992-Venturini #classification #named

AGIL: Solving the Exploration Versus Exploration Dilemma in a single Classifier System Applied to Simulated Robotics (GV), pp. 458–463.

ML-1992-WeinbergBK #clustering #concept

Conceptual Clustering with Systematic Missing Values (JBW, GB, GRK), pp. 464–469.

ML-1992-Zhang #learning

Selecting Typical Instances in Instance-Based Learning (JZ), pp. 470–479.

ML-1992-ZytkowZZ #fault

The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments (JMZ, JZ, RZ), pp. 480–485.