00987nam a2200277 i 450099100106969970753620020507182639.0971105s1996 de ||| | eng 3540589775b1079668x-39ule_instLE01306560ExLDip.to Matematicaeng514.72AMS 57R65Farrell, F. T.535896Lectures on surgical methods in rigidity /F. T. FarrellBerlin :Publ. for the Tata Inst. of Fund. Research by Springer,199698 p. ;24 cm.Includes bibliographical referencesSurgery.b1079668x21-09-0628-06-02991001069699707536LE013 57R FAR11 (1996)12013000091181le013-E0.00-l- 01010.i1089819028-06-02Lectures on surgical methods in rigidity921621UNISALENTOle01301-01-97ma -engde 0100983nam a2200253 i 450099100310060970753620020503183440.0970304s1990 it ||| | ita 8820465833b10458116-39ule_instEXGIL115109ExLDip.to Filol. Ling. e Lett.itaSensales, Gilda272805L'informatica nella stampa italiana :le comunicazioni di massa nel processo psico-sociale delle rappresentazioni /Gilda SensalesMilano :F. Angeli,1990137 p. ;22 cm.Informatica - linguisticaLinguistica - informatica.b1045811621-09-0627-06-02991003100609707536LE008 FL.M. (L.G.) H 3212008000407889le008-E0.00-l- 00000.i1053059927-06-02Informatica nella stampa italiana221832UNISALENTOle00801-01-97ma -itait 2103402nam 22005175 450 991048409300332120200630042223.03-319-93752-910.1007/978-3-319-93752-6(CKB)3850000000033395(DE-He213)978-3-319-93752-6(MiAaPQ)EBC5917842(PPN)229493734(EXLCZ)99385000000003339520180620d2019 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierDecision Tree and Ensemble Learning Based on Ant Colony Optimization /by Jan Kozak1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XI, 159 p. 44 illus.) Studies in Computational Intelligence,1860-949X ;7813-319-93751-0 Theoretical Framework -- Evolutionary Computing Techniques in Data Mining -- Ant Colony Decision Tree Approach -- Adaptive Goal Function of the ACDT Algorithm -- Examples of Practical Application.This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.Studies in Computational Intelligence,1860-949X ;781Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.519.6Kozak Janauthttp://id.loc.gov/vocabulary/relators/aut410838MiAaPQMiAaPQMiAaPQBOOK9910484093003321Decision Tree and Ensemble Learning Based on Ant Colony Optimization2854697UNINA