Proceedings of the 10th International Conference on Machine Learning
ICML, 1993.

@proceedings{ICML-1993,
	address       = "Amherst, Massachusetts, USA",
	isbn          = "1-55860-307-7",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the 10th International Conference on Machine Learning}",
	year          = 1993,
}

Contents (44 items)

24 ×#learning
6 ×#network
5 ×#concept
5 ×#induction
3 ×#automation
3 ×#clustering
3 ×#modelling
3 ×#multi
3 ×#named
3 ×#scalability

ICML-1993-Baluja #algorithm #evolution #parallel #towards

The Evolution of Gennetic Algorithms: Towards Massive Parallelism (SB), pp. 1–8.

ICML-1993-BrezellecS #bottom-up #learning #named

ÉLÉNA: A Bottom-Up Learning Method (PB, HS), pp. 9–16.

ICML-1993-Brodley #automation

Automatic Algorith/Model Class Selection (CEB), pp. 17–24.

ICML-1993-Cardie #learning #using

Using Decision Trees to Improve Case-Based Learning (CC), pp. 25–32.

ICML-1993-CarpinetoR #approach #clustering #concept #named

GALOIS: An Order-Theoretic Approach to Conceptual Clustering (CC, GR), pp. 33–40.

ICML-1993-Caruana #bias #induction #knowledge-based #learning #multi

Multitask Learning: A Knowledge-Based Source of Inductive Bias (RC), pp. 41–48.

ICML-1993-ClarkM #induction #learning #modelling #using

Using Qualitative Models to Guide Inductive Learning (PC, SM), pp. 49–56.

ICML-1993-CohenCBA #analysis #automation #modelling

Automating Path Analysis for Building Causal Models from Data (PRC, AC, LB, ASA), pp. 57–64.

ICML-1993-Connolly #clustering #concept #network

Constructing Hidden Variables in Bayesian Networks via Conceptual Clustering (DC), pp. 65–72.

ICML-1993-CravenS #learning #network #using

Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.

ICML-1993-DanylukP #fault #learning #network

Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network (APD, FJP), pp. 81–88.

ICML-1993-DattaK #concept #learning #multi

Concept Sharing: A Means to Improve Multi-Concept Learning (PD, DFK), pp. 89–96.

ICML-1993-DzeroskiT

Discovering Dynamics (SD, LT), pp. 97–103.

ICML-1993-Ellman #abstraction #approximate #clustering #constraints #synthesis

Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximately Equivalent Objects (TE), pp. 104–111.

ICML-1993-FayyadWD #automation #machine learning #named #scalability

SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys (UMF, NW, SGD), pp. 112–119.

ICML-1993-FrazierP #learning

Learning From Entailment: An Application to Propositional Horn Sentences (MF, LP), pp. 120–127.

ICML-1993-Gil #independence #performance

Efficient Domain-Independent Experimentation (YG), pp. 128–134.

ICML-1993-GratchCD #learning #network #scheduling

Learning Search Control Knowledge for Deep Space Network Scheduling (JG, SAC, GD), pp. 135–142.

ICML-1993-HuffmanL #interactive #learning #natural language

Learning Procedures from Interactive Natural Language Instructions (SBH, JEL), pp. 143–150.

ICML-1993-Idestam-Almquist #anti #recursion

Generalization under Implication by Recursive Anti-unification (PIA), pp. 151–158.

ICML-1993-JordanJ #approach #divide and conquer #learning #statistics

Supervised Learning and Divide-and-Conquer: A Statistical Approach (MIJ, RAJ), pp. 159–166.

ICML-1993-Kaelbling #learning #probability

Hierarchical Learning in Stochastic Domains: Preliminary Results (LPK), pp. 167–173.

ICML-1993-KimR #learning

Constraining Learning with Search Control (JK, PSR), pp. 174–181.

ICML-1993-Lin #learning #scalability

Scaling Up Reinforcement Learning for Robot Control (LJL), pp. 182–189.

ICML-1993-McCallum #memory management

Overcoming Incomplete Perception with Util Distinction Memory (AM), pp. 190–196.

ICML-1993-MitchellT #comparison #learning #network

Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches (TMM, ST), pp. 197–204.

ICML-1993-Mladenic #combinator #concept #induction #learning #optimisation

Combinatorial Optimization in Inductive Concept Learning (DM), pp. 205–211.

ICML-1993-MusickCR #database #induction #scalability

Decision Theoretic Subsampling for Induction on Large Databases (RM, JC, SJR), pp. 212–219.

ICML-1993-NortonH #learning #probability

Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.

ICML-1993-ORorkeFE

Explaining and Generalizing Diagnostic Decisions (PO, YEF, ME), pp. 228–235.

ICML-1993-Quinlan #learning #modelling

Combining Instance-Based and Model-Based Learning (JRQ), pp. 236–243.

ICML-1993-RaoVF #data mining #mining

Data Mining of Subjective Agricultural Data (RBR, TBV, TWF), pp. 244–251.

ICML-1993-RagavanR #concept #learning #lookahead

Lookahead Feature Construction for Learning Hard Concepts (HR, LAR), pp. 252–259.

ICML-1993-RendersBS #adaptation #black box #how

Adaptive NeuroControl: How Black Box and Simple can it be (JMR, HB, MS), pp. 260–267.

ICML-1993-Rymon #induction #problem

An SE-tree based Characterization of the Induction Problem (RR), pp. 268–275.

ICML-1993-Salganicoff #adaptation #learning

Density-Adaptive Learning and Forgetting (MS), pp. 276–283.

ICML-1993-Schlimmer #algorithm

Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning (JCS), pp. 284–290.

ICML-1993-Schwalb #compilation #network

Compiling Bayesian Networks into Neural Networks (ES), pp. 291–297.

ICML-1993-Schwartz #learning

A Reinforcement Learning Method for Maximizing Undiscounted Rewards (AS), pp. 298–305.

ICML-1993-Schwartz93a #predict #scheduling

ATM SCheduling with Queuing Dely Predictions (DBS), pp. 306–313.

ICML-1993-SuttonW #learning #online #random

Online Learning with Random Representations (RSS, SDW), pp. 314–321.

ICML-1993-Tadepalli #bias #learning #query

Learning from Queries and Examples with Tree-structured Bias (PT), pp. 322–329.

ICML-1993-Tan #independence #learning #multi

Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents (MT), pp. 330–337.

ICML-1993-VanLehnJ #problem

Better Learners Use Analogical Problem Solving Sparingly (KV, RMJ), pp. 338–345.