Ivan Bratko, Saso Dzeroski
Proceedings of the 16th International Conference on Machine Learning
ICML, 1999.

@proceedings{ICML-1999,
	address       = "Bled, Slovenia",
	editor        = "Ivan Bratko and Saso Dzeroski",
	isbn          = "1-55860-612-2",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the 16th International Conference on Machine Learning}",
	year          = 1999,
}

Contents (54 items)

23 ×#learning
7 ×#using
6 ×#classification
5 ×#algorithm
4 ×#induction
4 ×#modelling
3 ×#approach
3 ×#markov
3 ×#naive bayes
2 ×#adaptation

ICML-1999-AbeL #concept #learning #linear #probability #using

Associative Reinforcement Learning using Linear Probabilistic Concepts (NA, PML), pp. 3–11.

ICML-1999-AbeN #internet #learning

Learning to Optimally Schedule Internet Banner Advertisements (NA, AN), pp. 12–21.

ICML-1999-BlanzieriR #classification #metric #nearest neighbour

A Minimum Risk Metric for Nearest Neighbor Classification (EB, FR), pp. 22–31.

ICML-1999-BontempiBB #learning #predict

Local Learning for Iterated Time-Series Prediction (GB, MB, HB), pp. 32–38.

ICML-1999-Bosch #abstraction #in memory #learning

Instance-Family Abstraction in Memory-Based Language Learning (AvdB), pp. 39–48.

ICML-1999-Boyan #difference #learning

Least-Squares Temporal Difference Learning (JAB), pp. 49–56.

ICML-1999-BrodieD #induction #learning #using

Learning to Ride a Bicycle using Iterated Phantom Induction (MB, GD), pp. 57–66.

ICML-1999-BurgardFJMT #mobile #scalability #using

Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM (WB, DF, HJ, CM, ST), pp. 67–76.

ICML-1999-CadezMSM #modelling

Hierarchical Models for Screening of Iron Deficiency Anemia (IVC, CEM, PS, GJM), pp. 77–86.

ICML-1999-CardieMP #classification #parsing

Combining Error-Driven Pruning and Classification for Partial Parsing (CC, SM, DRP), pp. 87–96.

ICML-1999-FanSZC #classification #named

AdaCost: Misclassification Cost-Sensitive Boosting (WF, SJS, JZ, PKC), pp. 97–105.

ICML-1999-FiroiuC #markov #modelling #using

Abstracting from Robot Sensor Data using Hidden Markov Models (LF, PRC), pp. 106–114.

ICML-1999-FrankW #using

Making Better Use of Global Discretization (EF, IHW), pp. 115–123.

ICML-1999-FreundM #algorithm #learning

The Alternating Decision Tree Learning Algorithm (YF, LM), pp. 124–133.

ICML-1999-Gama

Discriminant Trees (JG), pp. 134–142.

ICML-1999-GambergerLG

Experiments with Noise Filtering in a Medical Domain (DG, NL, CG), pp. 143–151.

ICML-1999-GervasioIL #adaptation #evaluation #learning #scheduling

Learning User Evaluation Functions for Adaptive Scheduling Assistance (MTG, WI, PL), pp. 152–161.

ICML-1999-GiordanaP #behaviour #on the

On Some Misbehaviour of Back-Propagation with Non-Normalized RBFNs and a Solution (AG, RP), pp. 162–170.

ICML-1999-Harries

Boosting a Strong Learner: Evidence Against the Minimum Margin (MBH), pp. 171–180.

ICML-1999-HuSK #detection #sequence

Detecting Motifs from Sequences (YJH, SBS, DFK), pp. 181–190.

ICML-1999-IijimaYYK #adaptation #behaviour #distributed #learning

Distributed Robotic Learning: Adaptive Behavior Acquisition for Distributed Autonomous Swimming Robot in Real World (DI, WY, HY, YK), pp. 191–199.

ICML-1999-Joachims #classification #using

Transductive Inference for Text Classification using Support Vector Machines (TJ), pp. 200–209.

ICML-1999-KimuraK #linear #performance

Efficient Non-Linear Control by Combining Q-learning with Local Linear Controllers (HK, SK), pp. 210–219.

ICML-1999-LangleyS #analysis #classification #naive bayes

Tractable Average-Case Analysis of Naive Bayesian Classifiers (PL, SS), pp. 220–228.

ICML-1999-LentL #learning #performance

Learning Hierarchical Performance Knowledge by Observation (MvL, JEL), pp. 229–238.

ICML-1999-Teng #semistructured data

Correcting Noisy Data (CMT), pp. 239–248.

ICML-1999-Meila #algorithm

An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data (MM), pp. 249–257.

ICML-1999-MladenicG #feature model #naive bayes

Feature Selection for Unbalanced Class Distribution and Naive Bayes (DM, MG), pp. 258–267.

ICML-1999-MorikBJ #approach #case study #knowledge-based #learning #monitoring #statistics

Combining Statistical Learning with a Knowledge-Based Approach — A Case Study in Intensive Care Monitoring (KM, PB, TJ), pp. 268–277.

ICML-1999-NgHR #policy #theory and practice

Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping (AYN, DH, SJR), pp. 278–287.

ICML-1999-PalhangS #induction #learning #logic programming

Learning Discriminatory and Descriptive Rules by an Inductive Logic Programming System (MP, AS), pp. 288–297.

ICML-1999-ParekhH #automaton

Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples (RP, VH), pp. 298–306.

ICML-1999-PeshkinMK #learning #memory management #policy

Learning Policies with External Memory (LP, NM, LPK), pp. 307–314.

ICML-1999-Pompe #induction #recursion

Noise-Tolerant Recursive Best-First Induction (UP), pp. 315–324.

ICML-1999-PriceB #learning #multi

Implicit Imitation in Multiagent Reinforcement Learning (BP, CB), pp. 325–334.

ICML-1999-RennieM #learning #using #web

Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.

ICML-1999-Robnik-SikonjaK #dependence #modelling

Attribute Dependencies, Understandability and Split Selection in Tree Based Models (MRS, IK), pp. 344–353.

ICML-1999-SakakibaraK #context-free grammar #learning #using

GA-based Learning of Context-Free Grammars using Tabular Representations (YS, MK), pp. 354–360.

ICML-1999-SchefferJ #analysis #fault

Expected Error Analysis for Model Selection (TS, TJ), pp. 361–370.

ICML-1999-SchneiderWMR #distributed

Distributed Value Functions (JGS, WKW, AWM, MAR), pp. 371–378.

ICML-1999-ScottM #classification #re-engineering

Feature Engineering for Text Classification (SS, SM), pp. 379–388.

ICML-1999-Talavera #clustering #feature model #preprocessor

Feature Selection as a Preprocessing Step for Hierarchical Clustering (LT), pp. 389–397.

ICML-1999-TalbertF #algorithm #named #optimisation

OPT-KD: An Algorithm for Optimizing Kd-Trees (DAT, DHF), pp. 398–405.

ICML-1999-ThompsonCM #information management #learning #natural language #parsing

Active Learning for Natural Language Parsing and Information Extraction (CAT, MEC, RJM), pp. 406–414.

ICML-1999-ThrunLF #learning #markov #modelling #monte carlo #parametricity #probability #process

Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes (ST, JL, DF), pp. 415–424.

ICML-1999-UtgoffS #approximate #unification

Approximation Via Value Unification (PEU, DJS), pp. 425–432.

ICML-1999-VaithyanathanD #clustering #documentation #learning

Model Selection in Unsupervised Learning with Applications To Document Clustering (SV, BD), pp. 433–443.

ICML-1999-VovkGS #algorithm

Machine-Learning Applications of Algorithmic Randomness (VV, AG, CS), pp. 444–453.

ICML-1999-Kadous #learning #multi

Learning Comprehensible Descriptions of Multivariate Time Series (MWK), pp. 454–463.

ICML-1999-WangM #markov #optimisation #process

Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes (GW, SM), pp. 464–473.

ICML-1999-WuBCS #induction #scalability

Large Margin Trees for Induction and Transduction (DW, KPB, NC, JST), pp. 474–483.

ICML-1999-Zhang #approach #learning

An Region-Based Learning Approach to Discovering Temporal Structures in Data (WZ), pp. 484–492.

ICML-1999-ZhengWT #lazy evaluation #learning #naive bayes

Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees (ZZ, GIW, KMT), pp. 493–502.

ICML-1999-ZhouB #algorithm #approach #hybrid #learning #memory management #parametricity #requirements

A Hybrid Lazy-Eager Approach to Reducing the Computation and Memory Requirements of Local Parametric Learning Algorithms (YZ, CEB), p. 503–?.