Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms Prerak Sanghvi Paper by: Hsinchun Chen Artificial Intelligence Lab, University of Arizona Journal of the American Society for Information Science, 1994 Source: Search on Google with key phrase “Text Classification Algorithms”

To explain the Algorithms, three examples have been discussed: –BackPropagation Neural Network –The symbolic ID3 / ID5R Algorithms –Evolution based Genetic Algorithms

With proper user-system interactions, these methods can greatly complement the prevailing full-text, keyword-based, probabilistic, and knowledge-based techniques.

Symbolic Learning and ID3 Symbolic machine learning techniques can be classified based on such underlying learning strategies as rote learning, learning by being told, learning by analogy, learning from examples, and learning from discovery The most promising of these is learning by Example since it involves concept learning, and relies on past experience.

Neural Networks and Backpropagation Backpropagation networks have been extremely popular for their unique learning capability Good convergence is obtained if sufficient examples are provided Neural Networks are important since they seem to work in a large variety of domains

Simulated Evolution and Genetic Algorithms In such algorithms a population of individuals (potential solutions) undergoes a sequence of unary (mutation) and higher order (crossover) transformations These individuals strive for survival: a selection (reproduction) scheme, biased towards selecting fitter individuals, produces the individuals for the next generation After some number of generations the program converges - the best individual represents the optimum solution

Comparisons ID3 was faster than a Backpropagation net, but the Backpropagation net was more adaptive to noisy data sets using batch learning, Backpropagation performed as well as ID3, but it was more noise-resistant The results indicated that genetic search is, at best, equally efficient as faster variants of a Backpropagation algorithm in very small scale networks, but far less efficient in larger networks. However, it is also showed that using some domain- specific genetic operators to train the Backpropagation network, instead of using the conventional Backpropagation Delta learning rule, improved performance