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Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from Wikipedia. The experimental results show that our proposed approach can significantly improve the performances of the task in mixed-initiative...
Traditional acoustic echo cancelers use a linear model to represent the echo path. Nevertheless, many consumer devices include loudspeakers and audio power amplifiers that may generate significant nonlinear distortions, creating the need for acoustic echo cancelers to produce a nonlinear filter response. To address this issue, we propose a nonlinear acoustic echo cancellation algorithm based on the...
We examine the problem of approximating the mean of a set of vectors as a sparse linear combination of those vectors. This problem is motivated by a common methodology in machine learning where a probability distribution is represented as the sample mean of kernel functions. In applications where this kernel mean function is evaluated repeatedly, having a sparse approximation is essential for scalability...
Local patch-based models have been shown to be effective in numerous image processing applications and have become the core of the state-of-the-art denoising, inpainting and structural editing algorithms. Most such modeling approaches mainly rely on searching for similar patches in the set of available patches. However, the apparent similarity between sufficiently small (e.g., 5×5 pixels) image regions...
There is a growing interest in analyzing multineuron spike trains, which are spike timing data obtained from multiple neurons in the brain. Kernel methods have been successful in clustering and classification of single-neuron spike trains. We extend these methods to multineuron spike trains. Among various possible extensions, the mixture kernel was found to be most effective. The optimum parameter...
In this study a keystroke-based authentication system is implemented on a large-scale free-text keystroke data set, where cost effective kernel-based learning algorithms are designed to enable trade-off between computational cost and accuracy performance. The authentication process evaluates the user's typing behavior on a vocabulary of words, where the judgments based on each word are concatenated...
We present a semi-supervised algorithm for rescoring the output of a speech keyword search (KWS) system. Conventional loss functions such as squared-error and logistic loss are not suitable for optimizing the commonly-used KWS term-weighted value (TWV) performance metric. We derive a novel concave modified logistic log-likelihood function which lower-bounds TWV. We then use a manifold-regularized...
Data representation is a crucial issue in signal processing and machine learning. In this work, we propose to guide the learning process with a prior knowledge describing how similarities between examples are organized. This knowledge is encoded in a tree structure that represents nested groups of similarities that are the pyramids of kernels. We propose a framework that learns a Support Vector Machine...
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