LEADER 00859nam0-22003011i-450- 001 990007039100403321 005 20020131 035 $a000703910 035 $aFED01000703910 035 $a(Aleph)000703910FED01 035 $a000703910 100 $a20020131d1971----km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $ay-------001yy 200 1 $a<>Fund's concepts of convertibility$fJoseph Gold 210 $aWashington$cInternational Monetary Fund$d1971 215 $aVI, 63 p.$d24 cm 225 1 $aPamphlet series$fInternational Monetary Fund$v14 676 $a341$v20$zita 700 1$aGold,$bJoseph$0232243 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007039100403321 952 $aX F[1] 3 (14)$b108655$fFGBC 959 $aFGBC 996 $aFund's concepts of convertibility$9707506 997 $aUNINA LEADER 03402nam 22005175 450 001 9910484093003321 005 20200630042223.0 010 $a3-319-93752-9 024 7 $a10.1007/978-3-319-93752-6 035 $a(CKB)3850000000033395 035 $a(DE-He213)978-3-319-93752-6 035 $a(MiAaPQ)EBC5917842 035 $a(PPN)229493734 035 $a(EXLCZ)993850000000033395 100 $a20180620d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDecision Tree and Ensemble Learning Based on Ant Colony Optimization /$fby Jan Kozak 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 159 p. 44 illus.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v781 311 $a3-319-93751-0 327 $aTheoretical Framework -- Evolutionary Computing Techniques in Data Mining -- Ant Colony Decision Tree Approach -- Adaptive Goal Function of the ACDT Algorithm -- Examples of Practical Application. 330 $aThis book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v781 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.6 700 $aKozak$b Jan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0410838 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484093003321 996 $aDecision Tree and Ensemble Learning Based on Ant Colony Optimization$92854697 997 $aUNINA