## 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)

- 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–?.

31 ×#learning

9 ×#algorithm

8 ×#classification

8 ×#multi

8 ×#using

4 ×#performance

3 ×#analysis

3 ×#named

3 ×#network

2 ×#approach

9 ×#algorithm

8 ×#classification

8 ×#multi

8 ×#using

4 ×#performance

3 ×#analysis

3 ×#named

3 ×#network

2 ×#approach