Lorenza Saitta
Proceedings of the 13th International Conference on Machine Learning
ICML, 1996.

@proceedings{ICML-1996,
	address       = "Bari, Italy",
	editor        = "Lorenza Saitta",
	isbn          = "1-55860-419-7",
	publisher     = "{Morgan Kaufmann}",
	title         = "{Proceedings of the 13th International Conference on Machine Learning}",
	year          = 1996,
}

Contents (66 items)

30 ×#learning
6 ×#algorithm
6 ×#multi
5 ×#classification
5 ×#network
5 ×#using
4 ×#performance
3 ×#concept
3 ×#nearest neighbour
3 ×#online

ICML-1996-AbeL #learning #modelling #using #word

Learning Word Association Norms Using Tree Cut Pair Models (NA, HL), pp. 3–11.

ICML-1996-AkkusG #classification #nearest neighbour

K Nearest Neighbor Classification on Feature Projections (AA, HAG), pp. 12–19.

ICML-1996-BanderaVBHB #visual notation

Residual Q-Learning Applied to Visual Attention (CB, FJV, JMB, MEH, LCBI), pp. 20–27.

ICML-1996-Baum #towards

Toward a Model of Mind as a Laissez-Faire Economy of Idiots (EBB), pp. 28–36.

ICML-1996-BlanzieriK #learning #network #online

Learning Radial Basis Function Networks On-line (EB, PK), pp. 37–45.

ICML-1996-Bostrom #induction #logic programming #regular expression #source code

Theory-Guideed Induction of Logic Programs by Inference of Regular Languages (HB), pp. 46–53.

ICML-1996-BoutilierD #approximate #programming

Approximate Value Trees in Structured Dynamic Programming (CB, RD), pp. 54–62.

ICML-1996-BoyanM #evaluation #learning #scalability

Learning Evaluation Functions for Large Acyclic Domains (JAB, AWM), pp. 63–70.

ICML-1996-Burges

Simplified Support Vector Decision Rules (CJCB), pp. 71–77.

ICML-1996-CarbonaraS #knowledge base #performance #refinement

Improving the Efficiency of Knowledge Base Refinement (LC, DHS), pp. 78–86.

ICML-1996-Caruana #algorithm #learning #multi

Algorithms and Applications for Multitask Learning (RC), pp. 87–95.

ICML-1996-DietterichKM #framework #learning

Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.

ICML-1996-DomingosP #classification #independence

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (PMD, MJP), pp. 105–112.

ICML-1996-DonohoR #induction #using

Constructive Induction Using Fragmentary Knowledge (SKD, LAR), pp. 113–121.

ICML-1996-EmdeW #learning #relational

Relational Instance-Based Learning (WE, DW), pp. 122–130.

ICML-1996-EngelsonK #identification

Identifying the Information Contained in a Flawed Theory (SPE, MK), pp. 131–138.

ICML-1996-EzawaSN #learning #network #risk management

Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management (KJE, MS, SWN), pp. 139–147.

ICML-1996-FreundS #algorithm

Experiments with a New Boosting Algorithm (YF, RES), pp. 148–156.

ICML-1996-FriedmanG #learning #network

Discretizing Continuous Attributes While Learning Bayesian Networks (NF, MG), pp. 157–165.

ICML-1996-GeibelW #concept #learning #relational

Learning Relational Concepts with Decision Trees (PG, FW), pp. 166–174.

ICML-1996-GoetzKM #adaptation #learning #online

On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning (PG, SK, RM), pp. 175–181.

ICML-1996-GoldingR

Applying Winnow to Context-Sensitive Spelling Correction (ARG, DR), pp. 182–190.

ICML-1996-GoldmanS #algorithm #empirical

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns (SAG, SDS), pp. 191–199.

ICML-1996-GordonS #learning #parametricity #statistics

Nonparametric Statistical Methods for Experimental Evaluations of Speedup Learning (GJG, AMS), pp. 200–206.

ICML-1996-GreinerGR #classification #learning

Learning Active Classifiers (RG, AJG, DR), pp. 207–215.

ICML-1996-GreinerGK

Exploiting the Omission of Irrelevant Data (RG, AJG, AK), pp. 216–224.

ICML-1996-GrolimundG #memory management #nearest neighbour

Speeding-up Nearest Neighbour Memories: The Template Tree Case Memory Organisation (SG, JGG), pp. 225–233.

ICML-1996-Hekanaho #concept #learning

Background Knowledge in GA-based Concept Learning (JH), pp. 234–242.

ICML-1996-HelmboldSSW #multi #online #using

On-Line Portfolio Selection Using Multiplicative Updates (DPH, RES, YS, MKW), pp. 243–251.

ICML-1996-IttnerS

Non-Linear Decision Trees — NDT (AI, MS), pp. 252–257.

ICML-1996-JappyNG #horn clause #learning #robust #source code

Negative Robust Learning Results from Horn Clause Programs (PJ, RN, OG), pp. 258–265.

ICML-1996-KoenigS #distance #learning #navigation

Passive Distance Learning for Robot Navigation (SK, RGS), pp. 266–274.

ICML-1996-KahaviW #bias #composition

Bias Plus Variance Decomposition for Zero-One Loss Functions (RK, DW), pp. 275–283.

ICML-1996-KollerS #feature model #towards

Toward Optimal Feature Selection (DK, MS), pp. 284–292.

ICML-1996-Kubat

Second Tier for Decision Trees (MK), pp. 293–301.

ICML-1996-Lathrop #on the

On the Learnability of the Uncomputable (RHL), pp. 302–309.

ICML-1996-LittmanS #convergence

A Generalized Reinforcement-Learning Model: Convergence and Applications (MLL, CS), pp. 310–318.

ICML-1996-LiuS #approach #feature model #probability

A Probabilistic Approach to Feature Selection — A Filter Solution (HL, RS), pp. 319–327.

ICML-1996-Mahadevan #learning

Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning (SM), pp. 328–336.

ICML-1996-Munos #algorithm #convergence #learning

A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.

ICML-1996-OatesC #multi

Searching for Structure in Multiple Streams of Data (TO, PRC), pp. 346–354.

ICML-1996-OkamotoY #analysis #classification #nearest neighbour

Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains (SO, NY), pp. 355–363.

ICML-1996-OliverBW #learning #using

Unsupervised Learning Using MML (JJO, RAB, CSW), pp. 364–372.

ICML-1996-PendrithR #difference #learning

Actual Return Reinforcement Learning versus Temporal Differences: Some Theoretical and Experimental Results (MDP, MRKR), pp. 373–381.

ICML-1996-Perez #learning #representation

Representing and Learning Quality-Improving Search Control Knowledge (MAP), pp. 382–390.

ICML-1996-PerezR #concept #learning

Learning Despite Concept Variation by Finding Structure in Attribute-based Data (EP, LAR), pp. 391–399.

ICML-1996-RaviseS #evolution #fault

An Advanced Evolution Should Not Repeat its Past Errors (CR, MS), pp. 400–408.

ICML-1996-ReddyTR #composition #empirical #learning

Theory-guided Empirical Speedup Learning of Goal Decomposition Rules (CR, PT, SR), pp. 409–417.

ICML-1996-Roverso #abstraction #knowledge base #multi #scalability

Analogy Access by Mapping Spreading and Abstraction in Large, Multifunctional Knowledge Bases (DR), pp. 418–426.

ICML-1996-Saerens #fault #learning

Non Mean Square Error Criteria for the Training of Learning Machines (MS), pp. 427–434.

ICML-1996-SahamiHS #categorisation #model-to-text #multi

Applying the Multiple Cause Mixture Model to Text Categorization (MS, MAH, ES), pp. 435–443.

ICML-1996-Sebag #approach #bias

Delaying the Choice of Bias: A Disjunctive Version Space Approach (MS), pp. 444–452.

ICML-1996-SinghP #classification #learning #network #performance

Efficient Learning of Selective Bayesian Network Classifiers (MS, GMP), pp. 453–461.

ICML-1996-Suzuki #algorithm #learning #network #performance #using

Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique (JS), pp. 462–470.

ICML-1996-TadepalliO #approximate #domain model #learning #modelling #scalability

Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (PT, DO), pp. 471–479.

ICML-1996-TaeC #information management

Experimental Knowledge Acquisition for Planning (KST, DJC), pp. 480–488.

ICML-1996-ThrunO #algorithm #learning #multi

Discovering Structure in Multiple Learning Tasks: The TC Algorithm (ST, JO), pp. 489–497.

ICML-1996-Ting #predict

The Characterisation of Predictive Accuracy and Decision Combination (KMT), pp. 498–506.

ICML-1996-TirriKM #learning

Prababilistic Instance-Based Learning (HT, PK, PM), pp. 507–515.

ICML-1996-WallaceKD

Causal Discovery via MML (CSW, KBK, HD), pp. 516–524.

ICML-1996-Widmer #incremental #recognition

Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning (GW), pp. 525–533.

ICML-1996-WieringS

Solving POMDPs with Levin Search and EIRA (MW, JS), pp. 534–542.

ICML-1996-ZuckerG #learning #performance #representation

Representation Changes for Efficient Learning in Structural Domains (JDZ, JGG), pp. 543–551.

ICML-1996-Mannila #data mining #machine learning #mining

Data Mining and Machine Learning (HM), p. 555.

ICML-1996-Moore #learning

Reinforcement Learning in Factories: The Auton Project (AWM0), p. 556.

ICML-1996-Vapnik #statistics

Statistical Theory of Generalization (VV), p. 557.