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Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems

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This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans. Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans. Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests. The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.


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This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans. Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans. Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests. The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

24 review for Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems

  1. 5 out of 5

    Dalton Sweeney

    Informative but out of date. The section on expert systems is probably irrelevant now, as learning algorithms have taken off and far surpassed them since this book was published.

  2. 5 out of 5

    Jovany Agathe

  3. 5 out of 5

    D Wahyu

  4. 5 out of 5

    Egor

  5. 5 out of 5

    Josh

  6. 4 out of 5

    Juk

  7. 5 out of 5

    Marek

  8. 4 out of 5

    Ariful

  9. 4 out of 5

    Arazo Al-rose

  10. 4 out of 5

    NIKOLA

  11. 4 out of 5

    Ebuka

  12. 5 out of 5

    Amine

  13. 5 out of 5

    Liz

  14. 5 out of 5

    Jack

  15. 4 out of 5

    Elena

  16. 5 out of 5

    Roundcrisis

  17. 4 out of 5

    Jeremy Banta

  18. 4 out of 5

    Jeremy Banta

  19. 5 out of 5

    Esra Taşkın

  20. 5 out of 5

    Rana Zia

  21. 4 out of 5

    Anamarija Podrebarac

  22. 4 out of 5

    Igor

  23. 4 out of 5

    Carmen

  24. 5 out of 5

    K

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