03740nam a22004815i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001800172072001700190072002300207072001600230100008400246245011000330250001800440264006900458300006600527336002600593337002600619338003600645347002400681490006500705505036900770520158901139650003202728650002302760650002902783650004402812650004802856650002902904710004102933773002602974776003603000776003603036776003603072830007203108856004603180912001503226999001703241978-3-031-35347-5DE-He21320251030110810.0cr nn 008mamaa230714s2024    sz |    s    |||| 0|eng d  a97830313534759978-3-031-35347-57 a10.1007/978-3-031-35347-52doi 4aTK7867-7867.5 7aTJFC2bicssc 7aTEC0080102bisacsh 7aTJFC2thema1 aSasao, Tsutomu.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96553410aClassification Functions for Machine Learning and Data Miningh[electronic resource] /cby Tsutomu Sasao.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 144 p. 45 illus., 26 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSynthesis Lectures on Digital Circuits & Systems,x1932-31740 aIntroduction -- Definitions and Basic Properties -- Minimization of Variables: Exact Method -- Minimization of Variables: Heuristic Method -- Two-Class Functions -- Linear Decomposition -- Data Mining and Machine Learning -- Functions with Multi-Valued Inputs -- Easily Reconstructable Functions -- Functions with Continuous Variables -- References -- Conclusions.  aThis book introduces a novel perspective on machine learning, offering distinct advantages over neural network-based techniques. This approach boasts a reduced hardware requirement, lower power consumption, and enhanced interpretability. The applications of this approach encompass high-speed classifications, including packet classification, network intrusion detection, and exotic particle detection in high-energy physics. Moreover, it finds utility in medical diagnosis scenarios characterized by small training sets and imbalanced data. The resulting rule generated by this method can be implemented either in software or hardware. In the case of hardware implementation, circuit design can employ look-up tables (memory), rather than threshold gates. The methodology described in this book involves extracting a set of rules from a training set, composed of categorical variable vectors and their corresponding classes. Unnecessary variables are eliminated, and the rules are simplifiedbefore being transformed into a sum-of-products (SOP) form. The resulting SOP exhibits the ability to generalize and predict outputs for new inputs. The effectiveness of this approach is demonstrated through numerous examples and experimental results using the University of California-Irvine (UCI) dataset. This book is primarily intended for graduate students and researchers in the fields of logic synthesis, machine learning, and data mining. It assumes a foundational understanding of logic synthesis, while familiarity with linear algebra and statistics would be beneficial for readers. 0aElectronic circuits.965535 0aData mining.96941 0aMachine learning.95505914aElectronic Circuits and Systems.96553624aData Mining and Knowledge Discovery.96553724aMachine Learning.9550592 aSpringerLink (Online service)9655380 tSpringer Nature eBook08iPrinted edition:z978303135346808iPrinted edition:z978303135348208iPrinted edition:z9783031353499 0aSynthesis Lectures on Digital Circuits & Systems,x1932-317496553940uhttps://doi.org/10.1007/978-3-031-35347-5  aZDB-2-SXSC  c56224d56224