04621nmm a2200313 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100003500139245018700174250001700361260006300378300003300441505089600474505096801370505100502338653002803343653002803371700002803399041001903427989003803446490004503484028002603529856006803555082001003623520067403633EB000658538EBX0100000000000000051162000000000000000.0cr|||||||||||||||||||||140122 ||| eng a97835404836561 aBergadano, Francescoe[editor]00aMachine Learning: ECML-94hElektronische RessourcebEuropean Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedingscedited by Francesco Bergadano, Luc de Raedt a1st ed. 1994 aBerlin, HeidelbergbSpringer Berlin Heidelbergc1994, 1994 aXIII, 447 pbonline resource0 aLearning belief network structure from data under causal insufficiency -- Cost-sensitive pruning of decision trees -- An instance-based learning method for databases: An information theoretic approach -- Early screening for gastric cancer using machine learning techniques -- DP1: Supervised and unsupervised clustering -- Using machine learning techniques to interpret results from discrete event simulation -- Flexible integration of multiple learning methods into a problem solving architecture -- Concept sublattices -- The piecewise linear classifier DIPOL92 -- Complexity of computing generalized VC-dimensions -- Learning relations without closing the world -- Properties of Inductive Logic Programming in function-free Horn logic -- Representing biases for Inductive Logic Programming -- Biases and their effects in Inductive Logic Programming -- Inductive learning of normal clauses0 aIndustrial applications of ML: Illustrations for the KAML dilemma and the CBR dream -- Knowledge representation in machine learning -- Inverting implication with small training sets -- A context similarity measure -- Incremental learning of control knowledge for nonlinear problem solving -- Characterizing the applicability of classification algorithms using meta-level learning -- Inductive learning of characteristic concept descriptions from small sets of classified examples -- FOSSIL: A robust relational learner -- A multistrategy learning system and its integration into an interactive floorplanning tool -- Bottom-up induction of oblivious read-once decision graphs -- Estimating attributes: Analysis and extensions of RELIEF -- BMWk revisited generalization and formalization of an algorithm for detecting recursive relations in term sequences -- An analytic and empirical comparison of two methods for discovering probabilistic causal relationships -- 0 aSample PAC-learnability in model inference -- Averaging over decision stumps -- Controlling constructive induction in CIPF: An MDL approach -- Using constraints to building version spaces -- On the utility of predicate invention in inductive logic programming -- Learning problem-solving concepts by reflecting on problem solving -- Existence and nonexistence of complete refinement operators -- A hybrid nearest-neighbor and nearest-hyperrectangle algorithm -- Automated knowledge acquisition for Prospector-like expert systems -- On the role of machine learning in knowledge-based control -- Discovering dynamics with genetic programming -- A geometric approach to feature selection -- Identifying unrecognizable regular languages by queries -- Intensional learning of logic programs -- Partially isomorphic generalization and analogical reasoning -- Learning from recursive, tree structured examples -- Concept formation in complex domains -- An algorithm for learning hierarchical classifiers -- aArtificial Intelligence aArtificial intelligence1 aRaedt, Luc dee[editor]07aeng2ISO 639-2 bSBAaSpringer Book Archives -20040 aLecture Notes in Artificial Intelligence50a10.1007/3-540-57868-440uhttps://doi.org/10.1007/3-540-57868-4?nosfx=yxVerlag3Volltext0 a006.3 aThis volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning. Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes. This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions. The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning