The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality of the feature space of documents have made this classification problem very difficult. We investigate four different methods for document classification: the naive Bayes classifier, the nearest neighbour classifier, decision trees and a subspace method. These were applied to seven-class Yahoo news groups (business, entertainment, health, international, politics, sports and technology) individually and in combination. We studied three classifier combination approaches: simple voting, dynamic classifier selection and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classifiers on our data sets. Combinations of multiple classifiers did not always improve the classification accuracy compared to the best individual classifier. Among the three different combination approaches, our adaptive classifier combination method introduced here performed the best. The best classification accuracy that we are able to achieve on this seven-class problem is approximately 83%, which is comparable to the performance of other similar studies. However, the classification problem considered here is more difficult because the pattern classes used in our experiments have a large overlap of words in their corresponding documents.

No abstract

Illumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction. In this paper, we present a novel and generalized learning framework for illumination-insensitive image representation, which can learn the discriminative features through maximizing the inter-difference and minimizing intradifference of the images with boosting. Particularly, we enhance the discriminative capacity of illuminationinsensitive image representation in three aspects. First, we learn a subset of different synergistic Weber excitation patterns (SWEP) to generate the dominant SWEP (DSWEP) and DSWEP codebook for exploring optimal illumination-insensitive patterns. Second, a compact DSWEP (C-DSWEP) is learned with a boosted set of weight to generate C-DSWEP codebook. Discriminative learning is aimed at robustness and compactness. Third, the discriminative histogram learning model is established for encoding CDSEP to further improve the discriminative ability and reduce redundancy. The extensive experiments on CMUPIE, FERET, Yale B, Yale B ext., LFW, and PhoTex databases have highlighted the superiority and the robustness of our method compared with some other state-of-the-art methods.