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An MLP classifier outputs a posterior probability for each class. With noisy data, classification becomes less certain, and the entropy of the posteriors distribution tends to increase providing a measure of classification confidence. However, at high nois ...
We propose a simple information-theoretic clustering approach based on maximizing the mutual information I(\sfx,y) between the unknown cluster labels y and the training patterns \sfx with respect to parameters of specifically constrained encoding dis ...
We designed the terminode routing protoc ol with the objective to scale in large mobile ad hoc networks where the topology, or node distribution, is irregular. Our routing protocol is a combination of two protocols: Terminode Local Routing (TLR - to reach ...
This paper investigates a new approach to perform simultaneous speech and speaker recognition. The likelihood estimated by a speaker identification system is combined with the posterior probability estimated by the speech recognizer. So, the joint posterio ...
This paper investigates a new approach to perform simultaneous speech and speaker recognition. The likelihood estimated by a speaker identification system is combined with the posterior probability estimated by the speech recognizer. So, the joint posterio ...
Methods to improve noise robustness of speech recognition systems often result in degradation of recognition performance for clean speech. Recently proposed Phase AutoCorrelation (PAC) \cite{ikbal03,ikbal03a} based features, showing noticeable improvement ...
This paper presents a probabilistic algorithm for segmenting and recognizing text embedded in video sequences based on adaptive thresholding using a Bayes filtering method. The algorithm approximates the posterior distribution of segmentation thresholds of ...
Methods to improve noise robustness of speech recognition systems often result in degradation of recognition performance for clean speech. Recently proposed Phase AutoCorrelation (PAC) \cite{ikbal03,ikbal03a} based features, showing noticeable improvement ...
We address the dielectric constant (or the polarizability for an isolated system) as obtained in density-functional supercell calculations with a discrete k-point sampling. We compare a scheme based on conventional perturbational theory to one based on a d ...
An MLP classifier outputs a posterior probability for each class. With noisy data classification becomes less certain and the entropy of the posteriors distribution tends to increase, therefore providing a measure of classification confidence. However, at ...