## William W. Cohen, Haym Hirsh

*Proceedings of the 11th International Conference on Machine Learning*

ICML, 1994.

@proceedings{ICML-1994, address = "New Brunswick, New Jersey, USA", editor = "William W. Cohen and Haym Hirsh", isbn = "1-55860-335-2", publisher = "{Morgan Kaufmann}", title = "{Proceedings of the 11th International Conference on Machine Learning}", year = 1994, }

### Contents (45 items)

- ICML-1994-AbeM #predict #probability
- A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars (NA, HM), pp. 3–11.
- ICML-1994-AhaLLM #learning #recursion #set
- Learning Recursive Relations with Randomly Selected Small Training Sets (DWA, SL, CXL, SM), pp. 12–18.
- ICML-1994-Asker
- Improving Accuracy of Incorrect Domain Theories (LA), pp. 19–27.
- ICML-1994-CaruanaF
- Greedy Attribute Selection (RC, DF), pp. 28–36.
- ICML-1994-CravenS #network #query #using
- Using Sampling and Queries to Extract Rules from Trained Neural Networks (MC, JWS), pp. 37–45.
- ICML-1994-Maza #architecture
- The Generate, Test, and Explain Discovery System Architecture (MdlM), pp. 46–52.
- ICML-1994-DruckerCJCV #algorithm #machine learning
- Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
- ICML-1994-Elomaa #learning
- In Defense of C4.5: Notes Learning One-Level Decision Trees (TE), pp. 62–69.
- ICML-1994-FurnkranzW #fault #incremental
- Incremental Reduced Error Pruning (JF, GW), pp. 70–77.
- ICML-1994-GervasioD #approach #incremental #learning
- An Incremental Learning Approach for Completable Planning (MTG, GD), pp. 78–86.
- ICML-1994-Gil #incremental #learning #refinement
- Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains (YG), pp. 87–95.
- ICML-1994-GiordanaSZ #algorithm #concept #learning #search-based
- Learning Disjunctive Concepts by Means of Genetic Algorithms (AG, LS, FZ), pp. 96–104.
- ICML-1994-Heger #learning
- Consideration of Risk in Reinformance Learning (MH), pp. 105–111.
- ICML-1994-HsuK #optimisation #query #semantics
- Rule Introduction for Semantic Query Optimization (CNH, CAK), pp. 112–120.
- ICML-1994-JohnKP #problem #set
- Irrelevant Features and the Subset Selection Problem (GHJ, RK, KP), pp. 121–129.
- ICML-1994-KietzL #algorithm #induction #logic programming #performance
- An Efficient Subsumption Algorithm for Inductive Logic Programming (JUK, ML), pp. 130–138.
- ICML-1994-KoppelSF
- Getting the Most from Flawed Theories (MK, AMS, RF), pp. 139–147.
- ICML-1994-LewisC #learning #nondeterminism
- Heterogenous Uncertainty Sampling for Supervised Learning (DDL, JC), pp. 148–156.
- ICML-1994-Littman #framework #game studies #learning #markov #multi
- Markov Games as a Framework for Multi-Agent Reinforcement Learning (MLL), pp. 157–163.
- ICML-1994-Mahadevan #case study #learning
- To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning (SM), pp. 164–172.
- ICML-1994-MahoneyM
- Comparing Methods for Refining Certainty-Factor Rule-Bases (JJM, RJM), pp. 173–180.
- ICML-1994-Mataric #learning
- Reward Functions for Accelerated Learning (MJM), pp. 181–189.
- ICML-1994-MooreL #algorithm #fault #performance #validation
- Efficient Algorithms for Minimizing Cross Validation Error (AWM, MSL), pp. 190–198.
- ICML-1994-MurphyP
- Revision of Production System Rule-Bases (PMM, MJP), pp. 199–207.
- ICML-1994-OpitzS #knowledge-based #network #search-based #using
- Using Genetic Search to Refine Knowledge-based Neural Networks (DWO, JWS), pp. 208–216.
- ICML-1994-PazzaniMMAHB #classification
- Reducing Misclassification Costs (MJP, CJM, PMM, KMA, TH, CB), pp. 217–225.
- ICML-1994-PengW #incremental #multi
- Incremental Multi-Step Q-Learning (JP, RJW), pp. 226–232.
- ICML-1994-Quinlan #category theory
- The Minimum Description Length Principle and Categorical Theories (JRQ), pp. 233–241.
- ICML-1994-RachlinKSA #comprehension #reasoning #towards
- Towards a Better Understanding of Memory-based Reasoning Systems (JR, SK, SS, DWA), pp. 242–250.
- ICML-1994-RoscaB #programming #search-based #self
- Hierarchical Self-Organization in Genetic programming (JPR, DHB), pp. 251–258.
- ICML-1994-Schaffer #performance
- A Conservation Law for Generalization Performance (CS), pp. 259–265.
- ICML-1994-SchapireW #algorithm #analysis #learning #on the #worst-case
- On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (RES, MKW), pp. 266–274.
- ICML-1994-Sebag #algorithm #constraints #induction
- A Constraint-based Induction Algorithm in FOL (MS), pp. 275–283.
- ICML-1994-SinghJJ #learning #markov #process
- Learning Without State-Estimation in Partially Observable Markovian Decision Processes (SPS, TSJ, MIJ), pp. 284–292.
- ICML-1994-Skalak #algorithm #feature model #prototype #random
- Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms (DBS), pp. 293–301.
- ICML-1994-TchoumatchenkoG #framework #learning
- A Baysian Framework to Integrate Symbolic and Neural Learning (IT, JGG), pp. 302–308.
- ICML-1994-ThamP #architecture #composition
- A Modular Q-Learning Architecture for Manipulator Task Decomposition (CKT, RWP), pp. 309–317.
- ICML-1994-Utgoff #algorithm #incremental #induction
- An Improved Algorithm for Incremental Induction of Decision Trees (PEU), pp. 318–325.
- ICML-1994-Valdes-PerezP #behaviour #heuristic
- A Powerful Heuristic for the Discovery of Complex Patterned Behaviour (REVP, AP), pp. 326–334.
- ICML-1994-WeissI
- Small Sample Decision tree Pruning (SMW, NI), pp. 335–342.
- ICML-1994-ZelleMK #bottom-up #induction #logic programming #top-down
- Combining Top-down and Bottom-up Techniques in Inductive Logic Programming (JMZ, RJM, JBK), pp. 343–351.
- ICML-1994-ZuckerG #concept #learning
- Selective Reformulation of Examples in Concept Learning (JDZ, JGG), pp. 352–360.
- ICML-1994-Jordan #approach #modelling #statistics
- A Statistical Approach to Decision Tree Modeling (MIJ), pp. 363–370.
- ICML-1994-Muggleton #induction #logic programming
- Bayesian Inductive Logic Programming (SM), pp. 371–379.
- ICML-1994-Pereira #bias #machine learning #natural language #problem
- Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing — Abstract (FCNP), p. 380.

14 ×#learning

8 ×#algorithm

5 ×#incremental

5 ×#induction

3 ×#logic programming

3 ×#performance

3 ×#search-based

2 ×#approach

2 ×#architecture

2 ×#concept

8 ×#algorithm

5 ×#incremental

5 ×#induction

3 ×#logic programming

3 ×#performance

3 ×#search-based

2 ×#approach

2 ×#architecture

2 ×#concept