## Carla E. Brodley, Andrea Pohoreckyj Danyluk

*Proceedings of the 18th International Conference on Machine Learning*

ICML-2001, 2001.

@proceedings{ICML-2001, address = "Williamstown, Massachusetts, USA", editor = "Carla E. Brodley and Andrea Pohoreckyj Danyluk", isbn = "1-55860-778-1", publisher = "{Morgan Kaufmann}", title = "{Proceedings of the 18th International Conference on Machine Learning}", year = 2001, }

### Contents (80 items)

- ICML-2001-AmarDGZ #learning #multi
- Multiple-Instance Learning of Real-Valued Data (RAA, DRD, SAG, QZ), pp. 3–10.
- ICML-2001-BlockeelS #algorithm #performance
- Efficient algorithms for decision tree cross-validation (HB, JS), pp. 11–18.
- ICML-2001-BlumC #graph #learning #using
- Learning from Labeled and Unlabeled Data using Graph Mincuts (AB, SC), pp. 19–26.
- ICML-2001-BowlingV #convergence #learning
- Convergence of Gradient Dynamics with a Variable Learning Rate (MHB, MMV), pp. 27–34.
- ICML-2001-ChajewskaKO #behaviour #learning
- Learning an Agent’s Utility Function by Observing Behavior (UC, DK, DO), pp. 35–42.
- ICML-2001-ChoiR #approximate #difference #fixpoint #learning #performance
- A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal Difference Learning (DC, BVR), pp. 43–50.
- ICML-2001-ChuKO #framework
- A Unified Loss Function in Bayesian Framework for Support Vector Regression (WC, SSK, CJO), pp. 51–58.
- ICML-2001-Codrington
- Boosting with Confidence Information (CWC), pp. 59–65.
- ICML-2001-CristianiniSL #kernel #semantics
- Latent Semantic Kernels (NC, JST, HL), pp. 66–73.
- ICML-2001-Das #feature model #hybrid
- Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection (SD), pp. 74–81.
- ICML-2001-Dearden
- Structured Prioritised Sweeping (RD), pp. 82–89.
- ICML-2001-DobraG #bias #classification
- Bias Correction in Classification Tree Construction (AD, JG), pp. 90–97.
- ICML-2001-DomeniconiG #approach #approximate #classification #dataset #multi #nearest neighbour #performance #query #scalability
- An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets (CD, DG), pp. 98–105.
- ICML-2001-DomingosH #algorithm #clustering #machine learning #scalability
- A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (PMD, GH), pp. 106–113.
- ICML-2001-DominguezJ #development #visual notation
- Visual Development and the Acquisition of Binocular Disparity Sensitivities (MD, RAJ), pp. 114–121.
- ICML-2001-DruckerSG #feedback #using
- Relevance Feedback using Support Vector Machines (HD, BS, DCG), pp. 122–129.
- ICML-2001-Eliassi-RadS #approach #information management
- A Theory-Refinement Approach to Information Extraction (TER, JWS), pp. 130–137.
- ICML-2001-EngelM #embedded #learning #markov #process
- Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.
- ICML-2001-Furnkranz #learning
- Round Robin Rule Learning (JF), pp. 146–153.
- ICML-2001-GartnerF #classification #named
- WBCsvm: Weighted Bayesian Classification based on Support Vector Machines (TG, PAF), pp. 154–161.
- ICML-2001-Geibel #bound #learning
- Reinforcement Learning with Bounded Risk (PG), pp. 162–169.
- ICML-2001-GetoorFKT #learning #modelling #probability #relational
- Learning Probabilistic Models of Relational Structure (LG, NF, DK, BT), pp. 170–177.
- ICML-2001-GhaniSY #categorisation #hypermedia #using
- Hypertext Categorization using Hyperlink Patterns and Meta Data (RG, SS, YY), pp. 178–185.
- ICML-2001-GhavamzadehM #learning
- Continuous-Time Hierarchical Reinforcement Learning (MG, SM), pp. 186–193.
- ICML-2001-GlickmanS #learning #memory management #policy #probability #search-based
- Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State (MRG, KPS), pp. 194–201.
- ICML-2001-HamerlyE #predict
- Bayesian approaches to failure prediction for disk drives (GH, CE), pp. 202–209.
- ICML-2001-Hutter #bound #predict #sequence
- General Loss Bounds for Universal Sequence Prediction (MH), pp. 210–217.
- ICML-2001-IvanovBP
- Expectation Maximization for Weakly Labeled Data (YAI, BB, AP), pp. 218–225.
- ICML-2001-JafariGGE #equilibrium #game studies #learning #nash #on the
- On No-Regret Learning, Fictitious Play, and Nash Equilibrium (AJ, AG, DG, GE), pp. 226–233.
- ICML-2001-Jiang #aspect-oriented #semistructured data
- Some Theoretical Aspects of Boosting in the Presence of Noisy Data (WJ), pp. 234–241.
- ICML-2001-JinH #approach #information retrieval #learning #word
- Learning to Select Good Title Words: An New Approach based on Reverse Information Retrieval (RJ, AGH), pp. 242–249.
- ICML-2001-JoachimsCS #categorisation #hypermedia #kernel
- Composite Kernels for Hypertext Categorisation (TJ, NC, JST), pp. 250–257.
- ICML-2001-KramerR
- Feature Construction with Version Spaces for Biochemical Applications (SK, LDR), pp. 258–265.
- ICML-2001-Krawiec #comparison #learning
- Pairwise Comparison of Hypotheses in Evolutionary Learning (KK), pp. 266–273.
- ICML-2001-KriegerLW #semistructured data
- Boosting Noisy Data (AK, CL, AJW), pp. 274–281.
- ICML-2001-LaffertyMP #modelling #probability #random #sequence
- Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data (JDL, AM, FCNP), pp. 282–289.
- ICML-2001-LangfordSM #bound #classification #predict
- An Improved Predictive Accuracy Bound for Averaging Classifiers (JL, MWS, NM), pp. 290–297.
- ICML-2001-LatinneSD #classification #multi #problem
- Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing (PL, MS, CD), pp. 298–305.
- ICML-2001-LawrenceS #kernel
- Estimating a Kernel Fisher Discriminant in the Presence of Label Noise (NDL, BS), pp. 306–313.
- ICML-2001-Lee #collaboration #learning #recommendation
- Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
- ICML-2001-Littman #game studies
- Friend-or-Foe Q-learning in General-Sum Games (MLL), pp. 322–328.
- ICML-2001-LiuECBT #3d #mobile #modelling #using
- Using EM to Learn 3D Models of Indoor Environments with Mobile Robots (YL, RE, DC, WB, ST), pp. 329–336.
- ICML-2001-LloraG #algorithm
- Inducing Partially-Defined Instances with Evolutionary Algorithms (XL, JMGiG), pp. 337–344.
- ICML-2001-MarchandS #learning #set
- Learning with the Set Covering Machine (MM, JST), pp. 345–352.
- ICML-2001-MarxDB #clustering #detection
- Coupled Clustering: a Method for Detecting Structural Correspondence (ZM, ID, JMB), pp. 353–360.
- ICML-2001-McGovernB #automation #learning #using
- Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density (AM, AGB), pp. 361–368.
- ICML-2001-NairCK #algorithm
- Some Greedy Algorithms for Sparse Nonlinear Regression (PBN, AC, AJK), pp. 369–376.
- ICML-2001-NgJ #classification #convergence #feature model
- Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection (AYN, MIJ), pp. 377–384.
- ICML-2001-NouretdinovMV
- Ridge Regression Confidence Machine (IN, TM, VV), pp. 385–392.
- ICML-2001-PapagelisK #using
- Breeding Decision Trees Using Evolutionary Techniques (AP, DK), pp. 393–400.
- ICML-2001-PellegM #clustering
- Mixtures of Rectangles: Interpretable Soft Clustering (DP, AWM), pp. 401–408.
- ICML-2001-PerkinsB #learning #set
- Lyapunov-Constrained Action Sets for Reinforcement Learning (TJP, AGB), pp. 409–416.
- ICML-2001-PrecupSD #approximate #difference #learning
- Off-Policy Temporal Difference Learning with Function Approximation (DP, RSS, SD), pp. 417–424.
- ICML-2001-RayP #multi
- Multiple Instance Regression (SR, DP), pp. 425–432.
- ICML-2001-Robnik-SikonjaK
- Comprehensible Interpretation of Relief’s Estimates (MRS, IK), pp. 433–440.
- ICML-2001-RoyM #estimation #fault #learning #reduction #towards
- Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
- ICML-2001-RozsypalK #algorithm #classification #nearest neighbour #search-based #using
- Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes (AR, MK), pp. 449–456.
- ICML-2001-SandM #estimation #modelling #using
- Repairing Faulty Mixture Models using Density Estimation (PS, AWM), pp. 457–464.
- ICML-2001-SarkarL #fuzzy #similarity
- Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series (MS, TYL), pp. 465–472.
- ICML-2001-SatoK #learning #markov #problem
- Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems (MS, SK), pp. 473–480.
- ICML-2001-SchefferW #incremental #information management #problem
- Incremental Maximization of Non-Instance-Averaging Utility Functions with Applications to Knowledge Discovery Problems (TS, SW), pp. 481–488.
- ICML-2001-SchwabacherL
- Discovering Communicable Scientific Knowledge from Spatio-Temporal Data (MS, PL), pp. 489–496.
- ICML-2001-SebastianiR #clustering
- Clustering Continuous Time Series (PS, MR), pp. 497–504.
- ICML-2001-SebbanNL #classification
- Boosting Neighborhood-Based Classifiers (MS, RN, SL), pp. 505–512.
- ICML-2001-SeldinBT #markov #memory management #segmentation #sequence
- Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources (YS, GB, NT), pp. 513–520.
- ICML-2001-ShakhnarovichEB #classification #evaluation #statistics
- Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation (GS, REY, YB), pp. 521–528.
- ICML-2001-SingerV #implementation #learning #performance
- Learning to Generate Fast Signal Processing Implementations (BS, MMV), pp. 529–536.
- ICML-2001-StoneS #learning #scalability #towards
- Scaling Reinforcement Learning toward RoboCup Soccer (PS, RSS), pp. 537–544.
- ICML-2001-StrensM #policy #statistics #testing #using
- Direct Policy Search using Paired Statistical Tests (MJAS, AWM), pp. 545–552.
- ICML-2001-TaoBW #approach #multi #network
- A Multi-Agent Policy-Gradient Approach to Network Routing (NT, JB, LW), pp. 553–560.
- ICML-2001-Thollard #algorithm #grammar inference #probability
- Improving Probabilistic Grammatical Inference Core Algorithms with Post-processing Techniques (FT), pp. 561–568.
- ICML-2001-Venkataraman #learning
- A procedure for unsupervised lexicon learning (AV), pp. 569–576.
- ICML-2001-WagstaffCRS #clustering
- Constrained K-means Clustering with Background Knowledge (KW, CC, SR, SS), pp. 577–584.
- ICML-2001-Wiering #learning #using
- Reinforcement Learning in Dynamic Environments using Instantiated Information (MW), pp. 585–592.
- ICML-2001-Wyatt #learning #using
- Exploration Control in Reinforcement Learning using Optimistic Model Selection (JLW), pp. 593–600.
- ICML-2001-XingJK #array #feature model
- Feature selection for high-dimensional genomic microarray data (EPX, MIJ, RMK), pp. 601–608.
- ICML-2001-ZadroznyE #classification #naive bayes #probability
- Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers (BZ, CE), pp. 609–616.
- ICML-2001-ZhangL #naive bayes
- Learnability of Augmented Naive Bayes in Nonimal Domains (HZ, CXL), pp. 617–623.
- ICML-2001-Zhang #approximate #bound #problem
- Some Sparse Approximation Bounds for Regression Problems (TZ0), pp. 624–631.
- ICML-2001-ZinkevichB #learning #markov #multi #process #symmetry
- Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning (MZ, TRB), p. 632–?.

27 ×#learning

11 ×#using

10 ×#classification

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6 ×#multi

5 ×#clustering

5 ×#probability

4 ×#approach

4 ×#approximate

4 ×#bound

11 ×#using

10 ×#classification

6 ×#algorithm

6 ×#multi

5 ×#clustering

5 ×#probability

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