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)

15 ×#learning
6 ×#using
5 ×#multi
4 ×#modelling
4 ×#predict
3 ×#adaptation
3 ×#approach
3 ×#case study
3 ×#classification
3 ×#induction

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.