## Armand Prieditis, Stuart J. Russell

*Proceedings of the 12th International Conference on Machine Learning*

ICML, 1995.

@proceedings{ICML-1995, address = "Tahoe City, California, USA", editor = "Armand Prieditis and Stuart J. Russell", isbn = "1-55860-377-8", publisher = "{Morgan Kaufmann}", title = "{Proceedings of the 12th International Conference on Machine Learning}", year = 1995, }

### Contents (71 items)

- ICML-1995-AbeLN #2d #algorithm #learning #online #using
- On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms (NA, HL, AN), pp. 3–11.
- ICML-1995-AlmuallimAK #learning #on the
- On Handling Tree-Structured Attributed in Decision Tree Learning (HA, YA, SK), pp. 12–20.
- ICML-1995-AuerHM #theory and practice
- Theory and Applications of Agnostic PAC-Learning with Small Decision Trees (PA, RCH, WM), pp. 21–29.
- ICML-1995-Baird #algorithm #approximate #learning
- Residual Algorithms: Reinforcement Learning with Function Approximation (LCBI), pp. 30–37.
- ICML-1995-BalujaC #algorithm #search-based #standard
- Removing the Genetics from the Standard Genetic Algorithm (SB, RC), pp. 38–46.
- ICML-1995-Benson #induction #learning #modelling
- Inductive Learning of Reactive Action Models (SB), pp. 47–54.
- ICML-1995-BlackmoreM #grid #incremental #network #visualisation
- Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network (JB, RM), pp. 55–63.
- ICML-1995-Blum #algorithm #empirical #scheduling
- Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain (AB), pp. 64–72.
- ICML-1995-Brodley #automation
- Automatic Selection of Split Criterion during Tree Growing Based on Node Location (CEB), pp. 73–80.
- ICML-1995-BrunkP #bias #semantics
- A Lexical Based Semantic Bias for Theory Revision (CB, MJP), pp. 81–89.
- ICML-1995-ChanS #comparative #evaluation
- A Comparative Evaluation of Voting and Meta-learning on Partitioned Data (PKC, SJS), pp. 90–98.
- ICML-1995-CichoszM #difference #learning #performance
- Fast and Efficient Reinforcement Learning with Truncated Temporal Differences (PC, JJM), pp. 99–107.
- ICML-1995-ClearyT #distance #using
- K*: An Instance-based Learner Using and Entropic Distance Measure (JGC, LET), pp. 108–114.
- ICML-1995-Cohen #effectiveness #induction #performance
- Fast Effective Rule Induction (WWC), pp. 115–123.
- ICML-1995-Cohen95a #categorisation #learning #relational
- Text Categorization and Relational Learning (WWC), pp. 124–132.
- ICML-1995-CrawH #network #refinement
- Protein Folding: Symbolic Refinement Competes with Neural Networks (SC, PH), pp. 133–141.
- ICML-1995-Cussens #algorithm #analysis #finite #learning
- A Bayesian Analysis of Algorithms for Learning Finite Functions (JC), pp. 142–149.
- ICML-1995-DaganE #classification #probability
- Committee-Based Sampling For Training Probabilistic Classifiers (ID, SPE), pp. 150–157.
- ICML-1995-DattaK #concept #learning #prototype
- Learning Prototypical Concept Descriptions (PD, DFK), pp. 158–166.
- ICML-1995-DeJong #case study
- A Case Study of Explanation-Based Control (GD), pp. 167–175.
- ICML-1995-DietterichF #learning #perspective
- Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
- ICML-1995-DonohoR #induction #lessons learnt
- Lessons from Theory Revision Applied to Constructive Induction (SKD, LAR), pp. 185–193.
- ICML-1995-DoughertyKS
- Supervised and Unsupervised Discretization of Continuous Features (JD, RK, MS), pp. 194–202.
- ICML-1995-Drakopoulos #bound #classification #fault #nearest neighbour
- Bounds on the Classification Error of the Nearest Neighbor Rule (JAD), pp. 203–208.
- ICML-1995-Duff #problem
- Q-Learning for Bandit Problems (MOD), pp. 209–217.
- ICML-1995-EngelsonK #reliability
- Distilling Reliable Information From Unreliable Theories (SPE, MK), pp. 218–225.
- ICML-1995-Fong
- A Quantitative Study of Hypothesis Selection (PWLF), pp. 226–234.
- ICML-1995-KrishnanLV #learning
- Learning to Make Rent-to-Buy Decisions with Systems Applications (PK, PML, JSV), pp. 233–330.
- ICML-1995-Fuchs #adaptation #heuristic #learning #parametricity #proving
- Learning Proof Heuristics by Adaptive Parameters (MF), pp. 235–243.
- ICML-1995-FultonKS #algorithm #multi #performance
- Efficient Algorithms for Finding Multi-way Splits for Decision Trees (TF, SK, SS), pp. 244–251.
- ICML-1995-GambardellaD #approach #learning #named #problem
- Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem (LMG, MD), pp. 252–260.
- ICML-1995-Gordon #approximate #programming
- Stable Function Approximation in Dynamic Programming (GJG), pp. 261–268.
- ICML-1995-Greiner #challenge
- The Challenge of Revising an Impure Theory (RG), pp. 269–277.
- ICML-1995-Hekanaho #concept #learning #multimodal
- Symbiosis in Multimodal Concept Learning (JH), pp. 278–285.
- ICML-1995-HerbsterW
- Tracking the Best Expert (MH, MKW), pp. 286–294.
- ICML-1995-KimuraYK #learning #probability
- Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward (HK, MY, SK), pp. 295–303.
- ICML-1995-KohaviJ #fault #parametricity
- Autmatic Parameter Selection by Minimizing Estimated Error (RK, GHJ), pp. 304–312.
- ICML-1995-KongD #bias
- Error-Correcting Output Coding Corrects Bias and Variance (EBK, TGD), pp. 313–321.
- ICML-1995-Lang #learning #named
- NewsWeeder: Learning to Filter Netnews (KL), pp. 331–339.
- ICML-1995-Lang95a #problem #search-based #synthesis
- Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza’s (KJL), pp. 340–343.
- ICML-1995-LangleyP
- Case-Based Acquisition of Place Knowledge (PL, KP), pp. 344–352.
- ICML-1995-Littlestone #algorithm #learning
- Comparing Several Linear-threshold Learning Algorithms on Tasks Involving Superfluous Attributes (NL), pp. 353–361.
- ICML-1995-LittmanCK #learning #policy #scalability
- Learning Policies for Partially Observable Environments: Scaling Up (MLL, ARC, LPK), pp. 362–370.
- ICML-1995-Lubinsky #classification #consistency #performance #using
- Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves (DJL), pp. 371–377.
- ICML-1995-MaassW #learning #performance
- Efficient Learning with Virtual Threshold Gates (WM, MKW), pp. 378–386.
- ICML-1995-McCallum #learning
- Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
- ICML-1995-MoriartyM #evolution #learning #performance
- Efficient Learning from Delayed Rewards through Symbiotic Evolution (DEM, RM), pp. 396–404.
- ICML-1995-Niyogi #complexity #learning
- Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions (PN), pp. 405–412.
- ICML-1995-NockG #learning #on the
- On Learning Decision Committees (RN, OG), pp. 413–420.
- ICML-1995-OliveiraS #graph #order
- Inferring Reduced Ordered Decision Graphs of Minimum Description Length (ALO, ALSV), pp. 421–429.
- ICML-1995-OliverH #on the
- On Pruning and Averaging Decision Trees (JJO, DJH), pp. 430–437.
- ICML-1995-Peng #performance #programming
- Efficient Memory-Based Dynamic Programming (JP), pp. 438–446.
- ICML-1995-PerezR #multi #using
- Using Multidimensional Projection to Find Relations (EP, LAR), pp. 447–455.
- ICML-1995-Pfahringer
- Compression-Based Discretization of Continuous Attributes (BP), pp. 456–463.
- ICML-1995-Quinlan
- MDL and Categorial Theories (Continued) (JRQ), pp. 464–470.
- ICML-1995-RaoGS #question
- For Every Generalization Action, Is There Really an Equal and Opposite Reaction? (RBR, DFG, WMS), pp. 471–479.
- ICML-1995-SalganicoffU #learning #multi #using
- Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices (MS, LHU), pp. 480–487.
- ICML-1995-Schmidhuber #complexity
- Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability (JS), pp. 488–496.
- ICML-1995-SinghP #algorithm #classification #comparison #induction
- A Comparison of Induction Algorithms for Selective and non-Selective Bayesian Classifiers (MS, GMP), pp. 497–505.
- ICML-1995-SmythGF #classification #estimation #kernel #using
- Retrofitting Decision Tree Classifiers Using Kernel Density Estimation (PS, AGG, UMF), pp. 506–514.
- ICML-1995-SquiresS #automation #machine learning #recognition
- Automatic Speaker Recognition: An Application of Machine Learning (BS, CS), pp. 515–521.
- ICML-1995-StreetMW #approach #induction #learning #predict
- An Inductive Learning Approach to Prognostic Prediction (WNS, OLM, WHW), pp. 522–530.
- ICML-1995-Sutton #modelling
- TD Models: Modeling the World at a Mixture of Time Scales (RSS), pp. 531–539.
- ICML-1995-TowellVGJ #information retrieval #learning
- Learning Collection FUsion Strategies for Information Retrieval (GGT, EMV, NKG, BJL), pp. 540–548.
- ICML-1995-Wang #approach #incremental #learning
- Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition (XW), pp. 549–557.
- ICML-1995-Weiss #learning
- Learning with Rare Cases and Small Disjuncts (GMW), pp. 558–565.
- ICML-1995-Wolpert
- Horizonal Generalization (DW), pp. 566–574.
- ICML-1995-YamazakiPM #ambiguity #learning #natural language
- Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.
- ICML-1995-Croft #information retrieval #machine learning
- Machine Learning and Information Retrieval (WBC), p. 587.
- ICML-1995-Heckerman #learning #network
- Learning With Bayesian Networks (DH), p. 588.
- ICML-1995-Pomerleau #learning
- Learning for Automotive Collision Avoidance and Autonomous Control (DP), p. 589.

30 ×#learning

8 ×#algorithm

7 ×#performance

6 ×#using

5 ×#classification

5 ×#induction

3 ×#approach

3 ×#multi

3 ×#network

3 ×#on the

8 ×#algorithm

7 ×#performance

6 ×#using

5 ×#classification

5 ×#induction

3 ×#approach

3 ×#multi

3 ×#network

3 ×#on the