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|a D97-1/00-040-2023E-PDF
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|a Artificial intelligence /
|c edited by Steven G. Krantz, Arni S.R. Srinivasa Rao, C.R. Rao.
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|a Cambridge, MA :
|b Academic Press,
|c 2023.
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|a 1 online resource.
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|b txt
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| 490 |
1 |
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|a Handbook of statistics ;
|v 49
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|a Intro -- Artificial Intelligence -- Copyright -- Contents -- Contributors -- Preface -- Part I: Foundations and methods -- Chapter 1: Object-oriented basis of artificial intelligence methodologies -- 1. OO in AI -- 1.1. The concept of object -- 1.2. Object member functions and mapping -- 1.3. Objects in mathematics -- 1.3.1. Object type and closure property -- 1.3.2. Objects and mathematical spaces -- 1.3.3. Objects in logic theory -- 1.4. ML-Vector objects -- 1.4.1. Nontrivial-Concept objects -- 1.4.2. Vectorization as the first step in ML formulation -- 1.4.3. Vector vs array
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| 505 |
8 |
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|a 1.4.4. Tensor object -- 1.5. Objects in AI state space -- 1.5.1. State space -- 1.5.2. State space search -- 1.5.2.1. Object operator -- 1.5.2.2. Context object -- 1.5.2.3. NEXT function -- 1.5.2.4. Score operator -- 1.5.3. State space search in evolutionary algorithms -- 1.6. Derivative-type objects-Automatic differentiation -- 2. Business requirements to ML problem formulation -- 2.1. ML problem formulation (nonsequence types) -- 2.2. ML formulation for sequence types -- 2.3. Overloaded terminology -- 2.4. Vector representation of common types of data
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|a 2.4.1. Choice of the word-Tensor or vector? -- 2.4.2. Vector representation of image -- 2.4.3. Vector representation of univariate time series signal -- 2.4.4. Vector representation of multivariate time series signal -- 2.4.5. Special type of multivariate time series-Video data -- 2.4.6. Vector representation for object detection images -- 2.4.7. Vector representation of nonhomogeneous features -- 2.4.8. Vector representation of text -- 2.5. Interesting ML problem statements -- 3. ML tools and implementation -- 4. ML Performance monitoring -- 5. Scope and limitation of the ML formulation
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|a 5.1. Experimental set up for deductive reasoning data sets -- 5.1.1. Data sets for selection problems -- 5.1.2. Data sets for matching problems -- 5.1.3. Data sets for divisibility problems -- 5.1.4. Data sets for representation problems -- 5.1.5. Data set for sorting problem -- 5.1.6. Machine learning models used in the study -- 5.1.6.1. Deep neural network -- 5.1.6.2. Random forest -- 5.1.6.3. Recurrent neural network -- 5.1.7. Train and test data set partitions -- 5.2. Observation of ML performance on deductive reasoning data sets
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|a 5.3. Interesting inferences of ML on deductive reasoning problems -- 6. Is the human brain the same as an artificial neural network? -- 6.1. Theoretical limitation of a computer with bound on time -- 6.2. Explainability deficit in a purely data-driven ML formulation -- 7. Summary of the chapter -- 8. Review questions -- Acknowledgement -- References -- Chapter 2: Machine learning in physics and geometry -- 1. Introduction and summary -- 1.1. Mathematical data as pure data -- 1.2. The inevitability of AI in geometry and physics -- 2. Background physics and mathematics -- 2.1. Polytopes
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|a Unrestricted online access.
|f Unrestricted online access.
|2 star
|5 CaOONL
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| 650 |
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|a Artificial intelligence.
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| 650 |
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6 |
|a Intelligence artificielle.
|0 (CaQQLa)201-0008626
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| 650 |
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7 |
|a artificial intelligence.
|2 aat
|0 (CStmoGRI)aat300251574
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| 655 |
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|a Electronic books.
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| 700 |
1 |
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|a Krantz, Steven G.
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| 700 |
1 |
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|a Rao, Arni S. R. Srinivasa.
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| 700 |
1 |
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|a Rao, C. R.
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| 776 |
0 |
8 |
|i Print version:
|z 9780443137648
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| 776 |
0 |
8 |
|i Print version:
|z 0443137633
|z 9780443137631
|w (OCoLC)1374095297
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| 830 |
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0 |
|a Handbook of statistics (Amsterdam, Netherlands) ;
|v v. 49.
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| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/handbooks/01697161/49
|z Texto completo
|
