Our latest paper, Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters, is now available at ScienceDirect and referenced through ACM. Here’s the abstract:
In this paper we present a comparative study of several methods that combine evolutionary algorithms and local search to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced here, that handles the architecture, the network’s initial weights and the training algorithm parameters. Our aim is to determine how the co-evolutive method can obtain better results from the point of view of running time and classification ability. Experimental results show that the co-evolutionary method obtains similar or better results than the other approaches, requiring far less training epochs and thus, reducing running time.
Just leave a comment here if you want a copy, or email us at jjmerelo (at) gmail (dot) com.
We chose this paper by Freeman and Yin because it includes two of our favourite topics: Self-Organizing Maps á la Kohonen and content management (something we dealt with in our paper Mapping weblog communities. Here’s the abstract:
We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space
All in all, an interesting paper, with little tweaking and a straightforward procedure to deal with large amount of documents. It presents what they call TOC, Topological Organization of Content, which is a hierarchical organization of self-organizing “strings”. At every level, the size of the string is computed using a Bayesian Information Criterion, and lower levels only deal with the documents of the node at the highest level. What we would like is to have a open-source implementation but, hey, you can’t have it all.