Book Image

Machine Learning in Java

By : Bostjan Kaluza
Book Image

Machine Learning in Java

By: Bostjan Kaluza

Overview of this book

<p>As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.</p> <p>Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.</p> <p>Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level.</p> <p>By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.</p>

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Machine Learning in Java
Bostjan Kaluza
Apr, 2016 | 258 pages
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Table of Contents (19 chapters)
Machine Learning in Java
Machine Learning in Java
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Applied Machine Learning Quick Start
Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
2
Java Libraries and Platforms for Machine Learning
Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
3
Basic Algorithms – Classification, Regression, and Clustering
Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
4
Customer Relationship Prediction with Ensembles
Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
5
Affinity Analysis
Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
6
Recommendation Engine with Apache Mahout
Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
7
Fraud and Anomaly Detection
Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
8
Image Recognition with Deeplearning4j
Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
9
Activity Recognition with Mobile Phone Sensors
Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
10
Text Mining with Mallet – Topic Modeling and Spam Detection
Text Mining with Mallet – Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
11
What is Next?
What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
References
References
Index
Index

Appendix A. References

The following are the references for all the citations throughout the book:

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  • KDD (n.d.). KDD Cup 2009: Customer relationship prediction. Retrieved from http://www.kdd.org/kdd-cup/view/kdd-cup-2009.

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  • Kurucz, M., Siklósi, D., Bíró, I., Csizsek, P., Fekete, Z., Iwatt, R., Kiss, T., and Szabó, A.. KDD Cup 2009 @ Budapest: feature partitioning and boosting 61. JMLR W&CP 7, 65–75. 2009.

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  • LKurgan, L.A. and Musilek, P.. A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review, 21(1), 1–24. 2006.

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  • Magalhães, P. Incorrect information provided by your website. 2010. Retrevied from http://www.best.eu.org/aboutBEST/helpdeskRequest.jsp?req=f5wpxc8&auth=Paulo.

  • Mariscal, G., Marban, O., and Fernandez, C.. A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review, 25(2), 137–166. 2010.

  • Mew, K. (2015). Android 5 Programming by Example. Birmingham, UK. Packt Publishing.

  • Miller, H., Clarke, S., Lane, S., Lonie, A., Lazaridis, D., Petrovski, S., and Jones, O.. Predicting customer behavior: The University of Melbourne's KDD Cup report, JMLR W&CP 7, 45–55. 2009.

  • Niculescu-Mizil, A., Perlich, C., Swirszcz, G., Sind- hwani, V., Liu, Y., Melville, P., Wang, D., Xiao, J., Hu, J., Singh, M., Shang, W. X., and Zhu, Y. F.. Winning the KDD Cup Orange Challenge with Ensemble Selection. JMLR W&CP, 7, 23–34. 2009. Retrieved from http://jmlr.org/proceedings/papers/v7/niculescu09/niculescu09.pdf.

  • Oracle (n.d.). Anomaly Detection. Retrieved from http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/anomalies.htm.

  • Osugi, T., Deng, K., and Scott, S.. Balancing exploration and exploitation: a new algorithm for active machine learning. Fifth IEEE International Conference on Data Mining, Houston, Texas. 2005.

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  • Rajak, A.. Association Rule Mining-Applications in Various Areas. 2008. Retrieved from https://www.researchgate.net/publication/238525379_Association_rule_mining-_Applications_in_various_areas.

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  • Sutton, R. S. and Barto, A. G.. Reinforcement Learning An Introduction. Cambridge, MA: MIT Press. 1998.

  • Tiwary, C.. Learning Apache Mahout. Birmingham, UK. Packt Publishing. 2015.

  • Tsai, J., Kaminka, G., Epstein, S., Zilka, A., Rika, I., Wang, X., Ogden, A., Brown, M., Fridman, N., Taylor, M., Bowring, E., Marsella, S., Tambe, M., and Sheel, A.. ESCAPES - Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison. Proceedings from 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011) 2 (6), 457–464. 2011. Retrieved from http://www.aamas-conference.org/Proceedings/aamas2011/papers/D3_G57.pdf.

  • Tsanas, A. and Xifara. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49, 560-567. 2012.

  • Utts, J.. What Educated Citizens Should Know About Statistics and Probability. The American Statistician, 57 (2), 74-79. 2003.

  • Wallach, H. M., Murray, I., Salakhutdinov, R., and Mimno, D.. Evaluation Methods for Topic Models. Proceedings from the 26th International conference on Machine Learning, Montreal, Canada. 2009. Retrieved from http://mimno.infosci.cornell.edu/papers/wallach09evaluation.pdf.

  • Witten, I. H. and Frank, E.. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. USA. Morgan Kaufmann Publishers. 2000.

  • Xie, J., Rojkova, V., Pal, S., and Coggeshall, S.. A Combination of Boosting and Bagging for KDD Cup 2009. JMLR W&CP, 7, 35–43. 2009.

  • Zhang, H.. The Optimality of Naive Bayes. Proceedings from FLAIRS 2004 conference. 2004. Retrieved from http://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf.

  • Ziegler, C-N., McNee, S. M., Konstan, J. A., and Lausen, G.. Improving Recommendation Lists Through Topic Diversification. Proceedings from the 14th International World Wide Web Conference (WWW '05), Chiba, Japan. 2005. Retrieved from http://www2.informatik.uni-freiburg.de/~cziegler/papers/WWW-05-CR.pdf.

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