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020 _a9783031353475
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024 7 _a10.1007/978-3-031-35347-5
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072 7 _aTEC008010
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100 1 _aSasao, Tsutomu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_965534
245 1 0 _aClassification Functions for Machine Learning and Data Mining
_h[electronic resource] /
_cby Tsutomu Sasao.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIII, 144 p. 45 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Digital Circuits & Systems,
_x1932-3174
505 0 _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.
520 _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.
650 0 _aElectronic circuits.
_965535
650 0 _aData mining.
_96941
650 0 _aMachine learning.
_955059
650 1 4 _aElectronic Circuits and Systems.
_965536
650 2 4 _aData Mining and Knowledge Discovery.
_965537
650 2 4 _aMachine Learning.
_955059
710 2 _aSpringerLink (Online service)
_965538
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031353468
776 0 8 _iPrinted edition:
_z9783031353482
776 0 8 _iPrinted edition:
_z9783031353499
830 0 _aSynthesis Lectures on Digital Circuits & Systems,
_x1932-3174
_965539
856 4 0 _uhttps://doi.org/10.1007/978-3-031-35347-5
912 _aZDB-2-SXSC
999 _c56224
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