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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
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Toc

Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review FREE CHAPTER 2. Practical Approach to Real-World Supervised Learning 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier A. Linear Algebra B. Probability Index

References

  1. Yarowsky, D (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196)
  2. Blum, A., and Mitchell, T (1998). Combining labeled and unlabeled data with co-training. COLT: Proceedings of the Workshop on Computational Learning Theory.
  3. Demiriz, A., Bennett, K., and Embrechts, M (1999). Semi-supervised clustering using genetic algorithms. Proceedings of Artificial Neural Networks in Engineering.
  4. Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux (2006). Label Propagation and Quadratic Criterion. In Semi-Supervised Learning, pp. 193-216
  5. T. Joachims (1998). Transductive Inference for Text Classification using Support Vector Machines, ICML.
  6. B. Settles (2008). Curious Machines: Active Learning with Structured Instances. PhD thesis, University of Wisconsin–Madison.
  7. D. Angluin (1988). Queries and concept learning. Machine Learning, 2:319–342.
  8. D. Lewis and...
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