Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.

In this paper, a novel visual signal reliability (VSR) measure is proposed to consider video degradation at the signal level in audio-visual speaker identification (AVSI). The VSR estimation is formulated using a~ Gaussian fuzzy membership function (GFMF) to measure lighting variations. The variance parameters of GFMF are optimized in order to maximize the performance of the overall AVSI. The experimental results show that the proposed method outperforms the score-based reliability measuring technique.

Many researches have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters.

Speech signals transmitted over telephone network often suffer from interference due to ambient noise and channel distortion. In this paper, a novel frame-dependent fuzzy channel compensation (FD-FCC) method employing two-stage bias subtraction is proposed to minimize the channel effect. First, through maximum likelihood (ML) estimation over the set of all word models, we choose the word model which is best matched with the input utterance. Then, based upon this word model, a set of mixture biases can be derived by averaging the cepstral differences between the input utterance and the chosen model. In the second stage, instead of using a single bias, a frame-dependent bias is calculated for each input frame to equalize the channel variations in the input utterance. This frame-dependent bias is achieved by the convex combination of those mixture biases which are weighted by a fuzzy membership function. Experimental results show that the channel effect can be effectively canceled even though the additive background noise is involved in a telephone speech recognition system.