Jude W. Shavlik
Proceedings of the 15th International Conference on Machine Learning
ICML, 1998.

@proceedings{ICML-1998,
	address       = "Madison, Wisconsin, USA",
	editor        = "Jude W. Shavlik",
	isbn          = "1-55860-556-8",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the 15th International Conference on Machine Learning}",
	year          = 1998,
}

Contents (66 items)

31 ×#learning
9 ×#algorithm
8 ×#classification
8 ×#multi
8 ×#using
4 ×#performance
3 ×#analysis
3 ×#named
3 ×#network
2 ×#approach

ICML-1998-AbeM #learning #query #using

Query Learning Strategies Using Boosting and Bagging (NA, HM), pp. 1–9.

ICML-1998-AlerBI #approach #learning #multi #programming #search-based

Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach (RA, DB, PI), pp. 10–18.

ICML-1998-AnglanoGBS #concept #evaluation #learning

An Experimental Evaluation of Coevolutive Concept Learning (CA, AG, GLB, LS), pp. 19–27.

ICML-1998-BaxterTW #named

KnightCap: A Chess Programm That Learns by Combining TD(λ) with Game-Tree Search (JB, AT, LW), pp. 28–36.

ICML-1998-Bay #classification #multi #nearest neighbour #set

Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets (SDB), pp. 37–45.

ICML-1998-BillsusP #collaboration #learning

Learning Collaborative Information Filters (DB, MJP), pp. 46–54.

ICML-1998-BlockeelRR #clustering #induction #top-down

Top-Down Induction of Clustering Trees (HB, LDR, JR), pp. 55–63.

ICML-1998-BollackerG #architecture #classification #reuse #scalability

A Supra-Classifier Architecture for Scalable Knowledge Reuse (KDB, JG), pp. 64–72.

ICML-1998-BonetG #learning #sorting

Learning Sorting and Decision Trees with POMDPs (BB, HG), pp. 73–81.

ICML-1998-BradleyM #feature model

Feature Selection via Concave Minimization and Support Vector Machines (PSB, OLM), pp. 82–90.

ICML-1998-BradleyF #clustering

Refining Initial Points for K-Means Clustering (PSB, UMF), pp. 91–99.

ICML-1998-Cesa-BianchiF #bound #finite #multi #problem

Finite-Time Regret Bounds for the Multiarmed Bandit Problem (NCB, PF), pp. 100–108.

ICML-1998-CristianiniSS #classification #scalability

Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space (NC, JST, PS), pp. 109–117.

ICML-1998-Dietterich #learning

The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.

ICML-1998-Domingos #heuristic

A Process-Oriented Heuristic for Model Selection (PMD), pp. 127–135.

ICML-1998-DzeroskiRB #learning #relational

Relational Reinforcement Learning (SD, LDR, HB), pp. 136–143.

ICML-1998-FrankW #generative #optimisation #set

Generating Accurate Rule Sets Without Global Optimization (EF, IHW), pp. 144–151.

ICML-1998-FrankW98a #mutation testing #permutation #using

Using a Permutation Test for Attribute Selection in Decision Trees (EF, IHW), pp. 152–160.

ICML-1998-Freitag #information management #learning #multi

Multistrategy Learning for Information Extraction (DF), pp. 161–169.

ICML-1998-FreundISS #algorithm #performance

An Efficient Boosting Algorithm for Combining Preferences (YF, RDI, RES, YS), pp. 170–178.

ICML-1998-FriedmanGL #classification #network #parametricity

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting (NF, MG, TJL), pp. 179–187.

ICML-1998-FriessCC #algorithm #kernel #learning #performance

The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines (TTF, NC, CC), pp. 188–196.

ICML-1998-GaborKS #learning #multi

Multi-criteria Reinforcement Learning (ZG, ZK, CS), pp. 197–205.

ICML-1998-Gama

Local Cascade Generalization (JG), pp. 206–214.

ICML-1998-GarciaN #algorithm #analysis #learning

A Learning Rate Analysis of Reinforcement Learning Algorithms in Finite-Horizon (FG, SMN), pp. 215–223.

ICML-1998-Gordon

Well-Behaved Borgs, Bolos, and Berserkers (DFG), pp. 224–232.

ICML-1998-Heskes #approach #learning #multi

Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach (TH), pp. 233–241.

ICML-1998-HuW #algorithm #framework #learning #multi

Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm (JH, MPW), pp. 242–250.

ICML-1998-JuilleP #case study #learning

Coevolutionary Learning: A Case Study (HJ, JBP), pp. 251–259.

ICML-1998-KearnsS #learning

Near-Optimal Reinforcement Learning in Polynominal Time (MJK, SPS), pp. 260–268.

ICML-1998-KearnsM #algorithm #bottom-up #performance

A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization (MJK, YM), pp. 269–277.

ICML-1998-KimuraK #algorithm #analysis #learning #using

An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function (HK, SK), pp. 278–286.

ICML-1998-KollerF #approximate #learning #probability #process #using

Using Learning for Approximation in Stochastic Processes (DK, RF), pp. 287–295.

ICML-1998-Lin #similarity

An Information-Theoretic Definition of Similarity (DL), pp. 296–304.

ICML-1998-LiquiereS #graph #machine learning

Structural Machine Learning with Galois Lattice and Graphs (ML, JS), pp. 305–313.

ICML-1998-LittmanJK #corpus #independence #learning #representation

Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus (MLL, FJ, GAK), pp. 314–322.

ICML-1998-LochS #markov #policy #process #using

Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes (JL, SPS), pp. 323–331.

ICML-1998-MargaritisT #3d #image #learning #sequence

Learning to Locate an Object in 3D Space from a Sequence of Camera Images (DM, ST), pp. 332–340.

ICML-1998-MaronR #classification #learning #multi

Multiple-Instance Learning for Natural Scene Classification (OM, ALR), pp. 341–349.

ICML-1998-McCallumN #classification #learning

Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.

ICML-1998-McCallumRMN #classification

Improving Text Classification by Shrinkage in a Hierarchy of Classes (AM, RR, TMM, AYN), pp. 359–367.

ICML-1998-McCluskeyW #case study #requirements #using #validation

A Case Study in the Use of Theory Revision in Requirements Validation (TLM, MMW), pp. 368–376.

ICML-1998-MitaimK #adaptation #fuzzy #probability

Stochastic Resonance with Adaptive Fuzzy Systems (SM, BK), pp. 377–385.

ICML-1998-MooreSBL #learning #named #optimisation

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions (AWM, JGS, JAB, MSL), pp. 386–394.

ICML-1998-NakamuraA #algorithm #collaboration #predict #using

Collaborative Filtering Using Weighted Majority Prediction Algorithms (AN, NA), pp. 395–403.

ICML-1998-Ng #feature model #learning #on the

On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples (AYN), pp. 404–412.

ICML-1998-NockJ #on the #power of

On the Power of Decision Lists (RN, PJ), pp. 413–420.

ICML-1998-PendrithM #analysis #learning #markov

An Analysis of Direct Reinforcement Learning in Non-Markovian Domains (MDP, MM), pp. 421–429.

ICML-1998-PiaterCZA #performance #random

A Randomized ANOVA Procedure for Comparing Performance Curves (JHP, PRC, XZ, MA), pp. 430–438.

ICML-1998-PrecupU #approximate #classification #using

Classification Using Phi-Machines and Constructive Function Approximation (DP, PEU), pp. 439–444.

ICML-1998-ProvostFK #algorithm #estimation #induction

The Case against Accuracy Estimation for Comparing Induction Algorithms (FJP, TF, RK), pp. 445–453.

ICML-1998-RamachandranM #network #refinement

Theory Refinement of Bayesian Networks with Hidden Variables (SR, RJM), pp. 454–462.

ICML-1998-RandlovA #learning #using

Learning to Drive a Bicycle Using Reinforcement Learning and Shaping (JR, PA), pp. 463–471.

ICML-1998-ReddyT #first-order #learning #source code

Learning First-Order Acyclic Horn Programs from Entailment (CR, PT), pp. 472–480.

ICML-1998-RyanP #architecture #composition #learning #named

RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.

ICML-1998-SalustowiczS #evolution #source code

Evolving Structured Programs with Hierarchical Instructions and Skip Nodes (RS, JS), pp. 488–496.

ICML-1998-SamuelCV #learning

An Investigation of Transformation-Based Learning in Discourse (KS, SC, KVS), pp. 497–505.

ICML-1998-Saul #automation #segmentation

Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time (LKS), pp. 506–514.

ICML-1998-SaundersGV #algorithm #learning

Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.

ICML-1998-SchneiderBM #scheduling

Value Function Based Production Scheduling (JGS, JAB, AWM), pp. 522–530.

ICML-1998-ShatkayK

Heading in the Right Direction (HS, LPK), pp. 531–539.

ICML-1998-Street #network #predict

A Neural Network Model for Prognostic Prediction (WNS), pp. 540–546.

ICML-1998-StuartB #learning

Learning the Grammar of Dance (JMS, EB), pp. 547–555.

ICML-1998-SuttonPS #learning

Intra-Option Learning about Temporally Abstract Actions (RSS, DP, SPS), pp. 556–564.

ICML-1998-TecuciK #education #student

Teaching an Agent to Test Students (GT, HK), pp. 565–573.

ICML-1998-WeissH #problem

The Problem with Noise and Small Disjuncts (GMW, HH), p. 574–?.