Apr 26, 2013

History of machine learning

The development of machine learning is an integral part of the development of artificial intelligence. In the early days of AI, people were interested in building machines that mimic human brains. The perceptron model was invented in 1957, and it generated over optimistic view for AI during 1960s. After Marvin Minsky pointed out the limitation of this model in expressing complex functions, researchers stopped pursuing this model for the next decade. .

In 1970s, the machine learning field was dormant, when expert systems became the mainstream approach in AI.  The revival of machine learning came in mid-1980s, when the decision tree model was invented and distributed as software. The model can be viewed by a human and is easy to explain. It is also very versatile and can adapt to widely different problems. It is also in mid 1980s multi-layer neural networks were invented, With enough hidden layers, a neural network can express any function, thus overcoming the limitation of perceptron. We see a revival of the neural network study.

Both decisions trees and neural networks see wide application in financial applications such as loan approval, fraud detection and portfolio management. They are also applied to a wide-range of industrial process and postal office automation (address recognition). 
Machine learning saw rapid growth in 1990s, due to the  invention of World-Wide-Web and large data gathered on the Internet. The fast interaction on the Intern called for more automation and more adaptivity in computer systems. Around 1995, SVM was proposed and have become quickly adopted. SVM packages like libSVM, SVM light make it a popular method to use. 

After year 2000, Logistic regression was rediscovered and re-designed for large scale machine learning problems . In the ten years following 2003, logistic regression has attracted a lot of research work and has become a practical algorithm in many large-scale commercial systems, particularly in large Internet companies.  

We discussed the development of 4 major machine learning methods. There are other method developed in parallel, but see declining use today in the machine field: Naive Bayes, Bayesian networks, and Maximum Entropy classifier (most used in natural language processing). 

In addition to the individual methods, we have seen the invention of ensemble learning, where several classifiers are used together, and its wide adoption today. 

New machine learning methods are still invented each day. For the newest development, please check out the annual ICML (International Conference on Machine Learning) conference. 


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