Bruce W. Porter, Raymond J. Mooney
Proceedings of the Seventh International Workshop on Machine Learning
ML, 1990.

@proceedings{ML-1990,
	address       = "Austin, Texas, USA",
	editor        = "Bruce W. Porter and Raymond J. Mooney",
	isbn          = "1-55860-141-4",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the Seventh International Workshop on Machine Learning}",
	year          = 1990,
}

Contents (50 items)

24 ×#learning
5 ×#incremental
4 ×#algorithm
4 ×#analysis
4 ×#induction
4 ×#performance
4 ×#using
3 ×#approach
3 ×#approximate
3 ×#concept

ML-1990-ArunkumarY #information management #learning #representation #using

Knowledge Acquisition from Examples using Maximal Representation Learning (SA, SY), pp. 2–8.

ML-1990-Bisson #knowledge base

KBG : A Knowledge Based Generalizer (GB), pp. 9–15.

ML-1990-ChanW #analysis #induction #learning #performance #probability

Performance Analysis of a Probabilistic Inductive Learning System (KCCC, AKCW), pp. 16–23.

ML-1990-DietterichHB #case study #comparative

A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping (TGD, HH, GB), pp. 24–31.

ML-1990-Hirsh #bound #consistency #learning #nondeterminism

Learning from Data with Bounded Inconsistency (HH), pp. 32–39.

ML-1990-Kadie #algorithm #concept #set

Conceptual Set Covering: Improving Fit-And-Split Algorithms (CMK), pp. 40–48.

ML-1990-SchoenauerS #incremental #learning

Incremental Learning of Rules and Meta-rules (MS, MS), pp. 49–57.

ML-1990-UtgoffB #incremental #multi

An Incremental Method for Finding Multivariate Splits for Decision Trees (PEU, CEB), pp. 58–65.

ML-1990-Velde #incremental #induction

Incremental Induction of Topologically Minimal Trees (WVdV), pp. 66–74.

ML-1990-AndersonM #analysis #categorisation

A Rational Analysis of Categorization (JRA, MM), pp. 76–84.

ML-1990-CarlsonWF #concept #induction

Search Control, Utility, and Concept Induction (BMC, JBW, DHF), pp. 85–92.

ML-1990-Segen #clustering #graph #learning

Graph Clustering and Model Learning by Data Compression (JS), pp. 93–101.

ML-1990-Cohen #analysis #concept #learning #representation

An Analysis of Representation Shift in Concept Learning (WWC), pp. 104–112.

ML-1990-Hume #induction #learning

Learning Procedures by Environment-Driven Constructive Induction (DVH), pp. 113–121.

ML-1990-RouveirolP

Beyond Inversion of Resolution (CR, JFP), pp. 122–130.

ML-1990-Garis #programming #search-based

Genetic Programming (HdG), pp. 132–139.

ML-1990-KadabaN #algorithm #automation #parametricity #performance #search-based

Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters (NK, KEN), pp. 140–148.

ML-1990-McCallumS #algorithm #search-based #using

Using Genetic Algorithms to Learn Disjunctive Rules from Examples (AM, KAS), pp. 149–152.

ML-1990-BonelliPSW #named #performance

Newboole: A Fast GBML System (PB, AP, SS, SWW), pp. 153–159.

ML-1990-Kaelbling #learning

Learning Functions in k-DNF from Reinforcement (LPK), pp. 162–169.

ML-1990-SammutC #learning #performance #question

Is Learning Rate a Good Performance Criterion for Learning? (CS, JC), pp. 170–178.

ML-1990-WhiteheadB #learning

Active Perception and Reinforcement Learning (SDW, DHB), pp. 179–188.

ML-1990-Epstein #learning

Learning Plans for Competitive Domains (SLE), pp. 190–197.

ML-1990-GordonG #empirical

Explanations of Empirically Derived Reactive Plans (DFG, JJG), pp. 198–203.

ML-1990-Hammond #learning #process

Learning and Enforcement: Stabilizing Environments to Facilitate Activity (KJH), pp. 204–210.

ML-1990-RamseyGS #contest #difference #learning

Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment (CLR, JJG, ACS), pp. 211–215.

ML-1990-Sutton #approximate #architecture #learning #programming

Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming (RSS), pp. 216–224.

ML-1990-Bennett #approximate

Reducing Real-world Failures of Approximate Explanation-based Rules (SWB), pp. 226–234.

ML-1990-LairdHYT #using

Correcting and Extending Domain Knowledge using Outside Guidance (JEL, MH, ESY, CMT), pp. 235–243.

ML-1990-Moore

Acquisition of Dynamic Control Knowledge for a Robotic Manipulator (AWM), pp. 244–252.

ML-1990-ThintW #clustering #feature model #modelling

Feature Extraction and Clustering of Tactile Impressions with Connectionist Models (MT, PPW), pp. 253–258.

ML-1990-Bostrom #approach #order

Generalizing the Order of Goals as an Approach to Generalizing Number (HB), pp. 260–267.

ML-1990-Cohen90a #approximate #learning

Learning Approximate Control Rules of High Utility (WWC), pp. 268–276.

ML-1990-Flann #abstraction

Applying Abstraction and Simplification to Learn in Intractable Domains (NSF), pp. 277–285.

ML-1990-GenestMP #approach #learning

Explanation-Based Learning with Incomplete Theories: A Three-step Approach (JG, SM, BP), pp. 286–294.

ML-1990-Kodratoff #abduction #problem #proving #using

Using Abductive Recovery of Failed Proofs for Problem Solving by Analogy (YK), pp. 295–303.

ML-1990-Minton #composition #design

Issues in the Design of Operator Composition Systems (SM), pp. 304–312.

ML-1990-Ram #incremental #learning

Incremental Learning of Explanation Patterns and Their Indices (AR), pp. 313–320.

ML-1990-BergadanoGSMB #learning

Integrated Learning in a real Domain (FB, AG, LS, DDM, FB), pp. 322–329.

ML-1990-Hish #incremental

Incremental Version-Space Merging (HH), pp. 330–338.

ML-1990-PazzaniS #algorithm #analysis #learning

Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.

ML-1990-SilverFIVB #framework #learning #multi

A Framework for Multi-Paradigmatic Learning (BS, WJF, GAI, JV, KB), pp. 348–356.

ML-1990-WuWZ #framework

An Integrated Framework of Inducing Rules from Examples (YW, SW, QZ), pp. 357–365.

ML-1990-Lehman #learning

A General Method for Learning Idiosyncratic Grammars (JFL), pp. 368–376.

ML-1990-LytinenM #comparison #learning

A Comparison of Learning Techniques in Second Language Learning (SLL, CEM), pp. 377–383.

ML-1990-KoMT #learning #string

Learning String Patterns and Tree Patterns from Examples (KIK, AM, WGT), pp. 384–391.

ML-1990-ObradovicP #learning #multi

Learning with Discrete Multi-Valued Neurons (ZO, IP), pp. 392–399.

ML-1990-Holder #machine learning #problem

The General Utility Problem in Machine Learning (LBH), pp. 402–410.

ML-1990-NordhausenL #approach #robust

A Robust Approach to Numeric Discovery (BN, PL), pp. 411–418.

ML-1990-Valtorta #complexity #knowledge base #network #refinement

More Results on the Complexity of Knowledge Base Refinement: Belief Networks (MV), pp. 419–426.