LEADER 01282nam--2200421---450- 001 990000760570203316 005 20060130143855.0 010 $a88-342-0101-9 035 $a0076057 035 $aUSA010076057 035 $a(ALEPH)000076057USA01 035 $a0076057 100 $a20011121d1988----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> paesaggi$fGustav Klimt$g[a cura] di Johannes Dobai$g[traduzione dal tedesco di Andrea Endrizzi con la collaborazione di Cristina Groff] 205 $a2. ed. 210 $aGardolo$cL. Reverdito$d1988 215 $a142 p$c1 p. di tav., ill.$d31 cm 312 $aDie Landschaften 410 $12001 454 $12001$aDie Landschaften$938756 676 $a759.36 700 1$aKLIMT,$bGustav$0222424 702 1$aDOBAI,$bJohannes 801 0$aIT$bsalbc$gISBD 912 $a990000760570203316 951 $aXII.2.C. 820(VII P 122)$b99025 LM$cVII P 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20011121$lUSA01$h1650 979 $aPATTY$b90$c20011121$lUSA01$h1650 979 $c20020403$lUSA01$h1723 979 $aPATRY$b90$c20040406$lUSA01$h1652 979 $aCOPAT5$b90$c20060130$lUSA01$h1438 996 $aDie Landschaften$938756 997 $aUNISA 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