Some Applications of Machine Learning

In this section I will discuss some machine learning algorithms and their application.  Will discuss supervised learning and unsupervised learning, active learning and transfer learning.

Supervised learning
In supervised learning the data is labelled, machine learning algorithm maps the input to the desired output to generate a model. This technique is commonly used to train neural networks and decision trees. Neural networks are mostly applied where there is previous data to learn from like in character recognition, image compression, stock market prediction etc.  While decision trees are also applied where there is previous data to learn from, they are mostly applied where there are decisions to be made like in product planning and loan applications.

Unsupervised learning
In unsupervised learning, machine learning algorithm draws inferences from datasets consisting of input data without labelled responses. This technique is based on data mining methods which include clustering and latent variable methods.   Some of the unsupervised learning applications are language understanding and image identification.

Active learning
Active learning is a semi supervised machine. In this technique, the learning algorithm is allowed to ask questions from an oracle.  The oracle is a human annotator which can assign labels to training instances, in return the learner uses the feedback to find or improve a model for the training data. They have been successfully applied in regression testing, fuzzy testing and inference of botnet protocols.

Transfer learning
Transfer learning system learns models with different “source” sampling distributions and training labels, and then transfers that knowledge to the target task (Perlich, Dalessandro, Raeder, Stitelman, & Provost, 2014). Transfer learning attempts to change this by developing methods to transfer knowledge learned in one or more source tasks and use it to improve learning in a related target task (Torrey & Shavlik, 2009). This technique has been successfully applied in online advertising.

Next we going to look at some available machine learning tools.

Reference:

1. Aarts, F., Kuppens, H., Tretmans, J., & Vaan, F. (2014). Improving active Mealy machine learning for protocol conformance testing. Machine Learning, 189-224.
2. http://www.aihorizon.com/essays/generalai/supervised_unsupervised_machine_learning.htm
3. Neural Networks, https://cs.stanford.edu/people/eroberts/courses/soco/projects/2000-01/neural-networks/Applications/index.html
4. Pete Swabey, February 24th 2014, from http://www.economistinsights.com/technology-innovation/opinion/%E2%80%9Cunsupervised-learning%E2%80%9D-and-future-analytics targeted display advertising: transfer learning in action. Machine Learning, 103-127.
5. Torrey, L., & Shavlik, J. (2009). Transfer Learning. Handbook of Research on Machine Learning Applications.
6. Unsupervised learning. (2016, March 30). In Wikipedia, The Free Encyclopedia. Retrieved 13:07, May 9, 2016, from https://en.wikipedia.org/w/index.php?title=Unsupervised_learning&oldid=712692172