Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

VERY few veterinary surgeons have thought fit to write a book on diseases of poultry. Mr. Ernest Gray has done justice to the subject and is to be congratulated on his effort. A book of this size, written by one with specialized knowledge, will add to the value of any library or private bookshelf. Diseases of Poultry Their Aetiology, Diagnosis, Treatment and Control; with a Section on the Normal Anatomy and Physiology of the Fowl. By Ernest Gray. (Lockwood's Agricultural and Horticultural Handbooks.) Pp. x + 198 + 16 plates. (London: Crosby Lockwood and Son, Ltd., 1940.) 9s. 6d. net.

Diseases of Poultry

/pdf/analysis-of-observed-chaotic-data-3spe9uweeb.pdf

Analysis of observed chaotic data

Today, a large number of audio features exists in audio retrieval for different purposes, such as automatic speech recognition, music information retrieval, audio segmentation, and environmental sound retrieval. The goal of this chapter is to review latest research in the context of audio feature extraction and to give an application-independent overview of the most important existing techniques. We survey state-of-the-art features from various domains and propose a novel taxonomy for the organization of audio features. Additionally, we identify the building blocks of audio features and propose a scheme that allows for the description of arbitrary features. We present an extensive literature survey and provide more than 200 references to relevant high-quality publications.

Features for Content-Based Audio Retrieval

Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis

Pathological assessment of patients’ speech signals using nonlinear dynamical analysis

Support vector wavelet adaptation for pathological voice assessment

A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

Introducing features that better represent the visual information of speakers during the speech production is still an open issue that highly affects the quality of the lip-reading and Audio Visual Speech Recognition (AVSR) tasks. In this paper, three different types of visual features from both the image-based and model-based ones are investigated inside a professional lip reading task. The simple raw gray level information of the lips Region of Interest (ROI), the geometric representation of lips shape and the Deep Bottle-neck Features (DBNFs) extracted from a 6-layer Deep Auto-encoder Neural Network (DANN) are three valuable feature sets compared while employed for the lip reading purpose. Two different recognition systems, including the conventional GMM-HMM and the state-of-the-art DNN-HMM hybrid, are utilized to perform an isolated and connected digit recognition task. The results indicate that the high level information extracted from deep layers of the lips ROI can represent the visual modality with advantage of “high amount of information in a low dimension feature vector”. Moreover, the DBNFs showed a relative improvement with an average of 15.4% in comparison to the shape features and the shape features showed a relative improvement with an average of 20.4% in comparison to the ROI features over the test data.

Farshad Almasganj

Papers

Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis

Pathological assessment of patients’ speech signals using nonlinear dynamical analysis

Support vector wavelet adaptation for pathological voice assessment

A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

Lip-reading via a DNN-HMM hybrid system using combination of the image-based and model-based features