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Table of contents of journal: *Machine learning

Results: 101-125/267
A LATTICE CONCEPTUAL CLUSTERING SYSTEM AND ITS APPLICATION TO BROWSING RETRIEVAL
Authors: CARPINETO C ROMANO G
Citation: C. Carpineto et G. Romano, A LATTICE CONCEPTUAL CLUSTERING SYSTEM AND ITS APPLICATION TO BROWSING RETRIEVAL, Machine learning, 24(2), 1996, pp. 95-122
BAGGING PREDICTORS
Authors: BREIMAN L
Citation: L. Breiman, BAGGING PREDICTORS, Machine learning, 24(2), 1996, pp. 123-140
UNIFYING INSTANCE-BASED AND RULE-BASED INDUCTION
Authors: DOMINGOS P
Citation: P. Domingos, UNIFYING INSTANCE-BASED AND RULE-BASED INDUCTION, Machine learning, 24(2), 1996, pp. 141-168
BEXA - A COVERING ALGORITHM FOR LEARNING PROPOSITIONAL CONCEPT DESCRIPTIONS
Authors: THERON H CLOETE I
Citation: H. Theron et I. Cloete, BEXA - A COVERING ALGORITHM FOR LEARNING PROPOSITIONAL CONCEPT DESCRIPTIONS, Machine learning, 24(1), 1996, pp. 5-40
TECHNICAL NOTE - SOME PROPERTIES OF SPLITTING CRITERIA
Authors: BREIMAN L
Citation: L. Breiman, TECHNICAL NOTE - SOME PROPERTIES OF SPLITTING CRITERIA, Machine learning, 24(1), 1996, pp. 41-47
STACKED REGRESSIONS
Authors: BREIMAN L
Citation: L. Breiman, STACKED REGRESSIONS, Machine learning, 24(1), 1996, pp. 49-64
ON LEARNING VISUAL CONCEPTS AND DNF FORMULAS
Authors: KUSHILEVITZ E ROTH D
Citation: E. Kushilevitz et D. Roth, ON LEARNING VISUAL CONCEPTS AND DNF FORMULAS, Machine learning, 24(1), 1996, pp. 65-85
SPECIAL ISSUE ON ROBOT LEARNING - INTRODUCTION
Authors: FRANKLIN JA MITCHELL TM THRUN S
Citation: Ja. Franklin et al., SPECIAL ISSUE ON ROBOT LEARNING - INTRODUCTION, Machine learning, 23(2-3), 1996, pp. 117-119
REAL-WORLD ROBOTICS - LEARNING TO PLAN FOR ROBUST EXECUTION
Authors: BENNETT SW DEJONG GF
Citation: Sw. Bennett et Gf. Dejong, REAL-WORLD ROBOTICS - LEARNING TO PLAN FOR ROBUST EXECUTION, Machine learning, 23(2-3), 1996, pp. 121-161
ROBOT PROGRAMMING BY DEMONSTRATION (RPD) - SUPPORTING THE INDUCTION BY HUMAN INTERACTION
Authors: FRIEDRICH H MUNCH S DILLMANN R BOCIONEK S SASSIN M
Citation: H. Friedrich et al., ROBOT PROGRAMMING BY DEMONSTRATION (RPD) - SUPPORTING THE INDUCTION BY HUMAN INTERACTION, Machine learning, 23(2-3), 1996, pp. 163-189
PERFORMANCE IMPROVEMENT OF ROBOT CONTINUOUS-PATH OPERATION THROUGH ITERATIVE LEARNING USING NEURAL NETWORKS
Authors: CHEN PCY MILLS JK SMITH KC
Citation: Pcy. Chen et al., PERFORMANCE IMPROVEMENT OF ROBOT CONTINUOUS-PATH OPERATION THROUGH ITERATIVE LEARNING USING NEURAL NETWORKS, Machine learning, 23(2-3), 1996, pp. 191-220
LEARNING CONTROLLERS FOR INDUSTRIAL ROBOTS
Authors: BAROGLIO C GIORDANA A KAISER M NUTTIN M PIOLA R
Citation: C. Baroglio et al., LEARNING CONTROLLERS FOR INDUSTRIAL ROBOTS, Machine learning, 23(2-3), 1996, pp. 221-249
ACTIVE LEARNING FOR VISION-BASED ROBOT GRASPING
Authors: SALGANICOFF M UNGAR LH BAJCSY R
Citation: M. Salganicoff et al., ACTIVE LEARNING FOR VISION-BASED ROBOT GRASPING, Machine learning, 23(2-3), 1996, pp. 251-278
PURPOSIVE BEHAVIOR ACQUISITION FOR A REAL ROBOT BY VISION-BASED REINFORCEMENT LEARNING
Authors: ASADA M NODA S TAWARATSUMIDA S HOSODA K
Citation: M. Asada et al., PURPOSIVE BEHAVIOR ACQUISITION FOR A REAL ROBOT BY VISION-BASED REINFORCEMENT LEARNING, Machine learning, 23(2-3), 1996, pp. 279-303
LEARNING CONCEPTS FROM SENSOR DATA OF A MOBILE ROBOT
Authors: KLINGSPOR V MORIK KJ RIEGER AD
Citation: V. Klingspor et al., LEARNING CONCEPTS FROM SENSOR DATA OF A MOBILE ROBOT, Machine learning, 23(2-3), 1996, pp. 305-332
REPRESENTATION OF FINITE-STATE AUTOMATA IN RECURRENT RADIAL BASIS FUNCTION NETWORKS
Authors: FRASCONI P GORI M MAGGINI M SODA G
Citation: P. Frasconi et al., REPRESENTATION OF FINITE-STATE AUTOMATA IN RECURRENT RADIAL BASIS FUNCTION NETWORKS, Machine learning, 23(1), 1996, pp. 5-32
SCALING-UP INDUCTIVE LEARNING WITH MASSIVE PARALLELISM
Authors: PROVOST FJ ARONIS JM
Citation: Fj. Provost et Jm. Aronis, SCALING-UP INDUCTIVE LEARNING WITH MASSIVE PARALLELISM, Machine learning, 23(1), 1996, pp. 33-46
CLASSIFICATION BY FEATURE PARTITIONING
Authors: GUVENIR HA SIRIN I
Citation: Ha. Guvenir et I. Sirin, CLASSIFICATION BY FEATURE PARTITIONING, Machine learning, 23(1), 1996, pp. 47-67
LEARNING IN THE PRESENCE OF CONCEPT DRIFT AND HIDDEN CONTEXTS
Authors: WIDMER G KUBAT M
Citation: G. Widmer et M. Kubat, LEARNING IN THE PRESENCE OF CONCEPT DRIFT AND HIDDEN CONTEXTS, Machine learning, 23(1), 1996, pp. 69-101
INDUCTIVE LOGIC PROGRAMMING - TECHNIQUES AND APPLICATIONS - REPLY
Authors: LAVRAC N DZEROSKI S
Citation: N. Lavrac et S. Dzeroski, INDUCTIVE LOGIC PROGRAMMING - TECHNIQUES AND APPLICATIONS - REPLY, Machine learning, 23(1), 1996, pp. 109-111
SPECIAL ISSUE ON REINFORCEMENT LEARNING
Authors: DIETTERICH TG
Citation: Tg. Dietterich, SPECIAL ISSUE ON REINFORCEMENT LEARNING, Machine learning, 22(1-3), 1996, pp. 5-6
SPECIAL ISSUE ON REINFORCEMENT LEARNING - INTRODUCTION
Authors: KAELBLING LP
Citation: Lp. Kaelbling, SPECIAL ISSUE ON REINFORCEMENT LEARNING - INTRODUCTION, Machine learning, 22(1-3), 1996, pp. 7-9
EFFICIENT REINFORCEMENT LEARNING THROUGH SYMBIOTIC EVOLUTION
Authors: MORIARTY DE MIIKKULAINEN R
Citation: De. Moriarty et R. Miikkulainen, EFFICIENT REINFORCEMENT LEARNING THROUGH SYMBIOTIC EVOLUTION, Machine learning, 22(1-3), 1996, pp. 11-32
LINEAR LEAST-SQUARES ALGORITHMS FOR TEMPORAL DIFFERENCE LEARNING
Authors: BRADTKE SJ BARTO AG
Citation: Sj. Bradtke et Ag. Barto, LINEAR LEAST-SQUARES ALGORITHMS FOR TEMPORAL DIFFERENCE LEARNING, Machine learning, 22(1-3), 1996, pp. 33-57
FEATURE-BASED METHODS FOR LARGE-SCALE DYNAMIC-PROGRAMMING
Authors: TSITSIKLIS JN VANROY B
Citation: Jn. Tsitsiklis et B. Vanroy, FEATURE-BASED METHODS FOR LARGE-SCALE DYNAMIC-PROGRAMMING, Machine learning, 22(1-3), 1996, pp. 59-94
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