## Douglas H. Fisher

*Proceedings of the 14th International Conference on Machine Learning*

ICML, 1997.

@proceedings{ICML-1997, address = "Nashville, Tennessee, USA", editor = "Douglas H. Fisher", isbn = "1-55860-486-3", publisher = "{Morgan Kaufmann}", title = "{Proceedings of the 14th International Conference on Machine Learning}", year = 1997, }

### Contents (48 items)

- ICML-1997-AskerM #case study #classification #detection #re-engineering
- Feature Engineering and Classifier Selection: A Case Study in Venusian Volcano Detection (LA, RM), pp. 3–11.
- ICML-1997-AtkesonS #learning
- Robot Learning From Demonstration (CGA, SS), pp. 12–20.
- ICML-1997-Auer #approach #empirical #evaluation #learning #multi #on the
- On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach (PA), pp. 21–29.
- ICML-1997-BalujaD #optimisation #using
- Using Optimal Dependency-Trees for Combinational Optimization (SB, SD), pp. 30–38.
- ICML-1997-Baxter #approximate #canonical
- The Canonical Distortion Measure for Vector Quantization and Function Approximation (JB), pp. 39–47.
- ICML-1997-BottaGP #first-order #learning #logic #named
- FONN: Combining First Order Logic with Connectionist Learning (MB, AG, RP), pp. 46–56.
- ICML-1997-CardieN #predict #using
- Improving Minority Class Prediction Using Case-Specific Feature Weights (CC, NN), pp. 57–65.
- ICML-1997-CohenD #case study #comparative #fault #induction #logic programming #predict
- A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction (WWC, PTD), pp. 66–74.
- ICML-1997-DattaK #learning #prototype
- Learning Symbolic Prototypes (PD, DFK), pp. 75–82.
- ICML-1997-Decatur #classification #induction #learning
- PAC Learning with Constant-Partition Classification Noise and Applications to Decision Tree Induction (SED), pp. 83–91.
- ICML-1997-DevaneyR #clustering #concept #feature model #performance
- Efficient Feature Selection in Conceptual Clustering (MD, AR), pp. 92–97.
- ICML-1997-Domingos #information management #modelling #multi
- Knowledge Acquisition form Examples Vis Multiple Models (PMD), pp. 98–106.
- ICML-1997-Drucker #using
- Improving Regressors using Boosting Techniques (HD), pp. 107–115.
- ICML-1997-Fiechter #bound #learning #online
- Expected Mistake Bound Model for On-Line Reinforcement Learning (CNF), pp. 116–124.
- ICML-1997-Friedman #learning #network
- Learning Belief Networks in the Presence of Missing Values and Hidden Variables (NF), pp. 125–133.
- ICML-1997-Gama #linear #probability
- Probabilistic Linear Tree (JG), pp. 134–142.
- ICML-1997-Joachims #algorithm #analysis #categorisation #probability
- A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization (TJ), pp. 143–151.
- ICML-1997-KimuraMK #approximate #learning
- Reinforcement Learning in POMDPs with Function Approximation (HK, KM, SK), pp. 152–160.
- ICML-1997-KohaviK
- Option Decision Trees with Majority Votes (RK, CK), pp. 161–169.
- ICML-1997-KollerS #documentation #using #word
- Hierarchically Classifying Documents Using Very Few Words (DK, MS), pp. 170–178.
- ICML-1997-KubatM #set
- Addressing the Curse of Imbalanced Training Sets: One-Sided Selection (MK, SM), pp. 179–186.
- ICML-1997-ManguB #automation
- Automatic Rule Acquisition for Spelling Correction (LM, EB), pp. 187–194.
- ICML-1997-Mansour
- Pessimistic decision tree pruning based Continuous-time (YM), pp. 202–210.
- ICML-1997-MargineantuD #adaptation
- Pruning Adaptive Boosting (DDM, TGD), pp. 211–218.
- ICML-1997-MayorazM #composition #on the
- On the Decomposition of Polychotomies into Dichotomies (EM, MM), pp. 219–226.
- ICML-1997-Menczer #adaptation #heuristic #named #retrieval
- ARCCHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods (FM), pp. 227–235.
- ICML-1997-MooreSD #performance #polynomial #predict
- Efficient Locally Weighted Polynomial Regression Predictions (AWM, JGS, KD), pp. 236–244.
- ICML-1997-Ng
- Preventing “Overfitting” of Cross-Validation Data (AYN), pp. 245–253.
- ICML-1997-OatesJ #complexity #set
- The Effects of Training Set Size on Decision Tree Complexity (TO, DJ), pp. 254–262.
- ICML-1997-Opitz #approach #component #effectiveness #network
- The Effective Size of a Neural Network: A Principal Component Approach (DWO), pp. 263–271.
- ICML-1997-PrecupS #learning
- Exponentiated Gradient Methods for Reinforcement Learning (DP, RSS), pp. 272–277.
- ICML-1997-ReddyT #learning #using
- Learning Goal-Decomposition Rules using Exercises (CR, PT), pp. 278–286.
- ICML-1997-RistadY #distance #edit distance #learning #string
- Learning String Edit Distance (ESR, PNY), pp. 287–295.
- ICML-1997-Robnik-SikonjaK #adaptation #estimation
- An adaptation of Relief for attribute estimation in regression (MRS, IK), pp. 296–304.
- ICML-1997-SakrLCHG #data access #learning #memory management #modelling #multi #predict
- Predicting Multiprocessor Memory Access Patterns with Learning Models (MFS, SPL, DMC, BGH, CLG), pp. 305–312.
- ICML-1997-Schapire #learning #multi #problem #using
- Using output codes to boost multiclass learning problems (RES), pp. 313–321.
- ICML-1997-SchapireFBL #effectiveness
- Boosting the margin: A new explanation for the effectiveness of voting methods (RES, YF, PB, WSL), pp. 322–330.
- ICML-1997-SchefferGD #why
- Why Experimentation can be better than “Perfect Guidance” (TS, RG, CD), pp. 331–339.
- ICML-1997-SchuurmansUF #performance
- Characterizing the generalization performance of model selection strategies (DS, LHU, DPF), pp. 340–348.
- ICML-1997-SuematsuHL #approach #learning #markov
- A Bayesian Approach to Model Learning in Non-Markovian Environments (NS, AH, SL), pp. 349–357.
- ICML-1997-TadepalliD #learning
- Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
- ICML-1997-TingW #modelling
- Stacking Bagged and Dagged Models (KMT, IHW), pp. 367–375.
- ICML-1997-TodorovskiD #bias #declarative #equation
- Declarative Bias in Equation Discovery (LT, SD), pp. 376–384.
- ICML-1997-Torgo #functional #modelling
- Functional Models for Regression Tree Leaves (LT), pp. 385–393.
- ICML-1997-VilaltaR #classification #induction #multi
- Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction (RV, LAR), pp. 394–402.
- ICML-1997-WilsonM
- Instance Pruning Techniques (DRW, TRM), pp. 403–411.
- ICML-1997-YangP #case study #categorisation #comparative #feature model
- A Comparative Study on Feature Selection in Text Categorization (YY, JOP), pp. 412–420.
- ICML-1997-ZupanBBD #composition #machine learning
- Machine Learning by Function Decomposition (BZ, MB, IB, JD), pp. 421–429.

15 ×#learning

6 ×#using

5 ×#multi

4 ×#modelling

4 ×#predict

3 ×#adaptation

3 ×#approach

3 ×#case study

3 ×#classification

3 ×#induction

6 ×#using

5 ×#multi

4 ×#modelling

4 ×#predict

3 ×#adaptation

3 ×#approach

3 ×#case study

3 ×#classification

3 ×#induction