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Depression detection using speech signal is becoming an attractive topic because it is fast, convenient and non-invasive. Many researches aimed at improving depression classification performance. This study investigated application of ensemble learners in depression detection and compared three speaking styles (interview, reading and picture description) in ensembles. A speech dataset collecting from...
Decision making is an important component in a speaker verification system. For the conventional GMM-UBM architecture, the decision is usually conducted based on the log likelihood ratio of the test utterance against the GMM of the claimed speaker and the UBM. This single-score decision is simple but tends to be sensitive to the complex variations in speech signals (e.g. text content, channel, speaking...
Statistical and machine learning methods have been proposed to predict hard drive failure based on SMART attributes, and many achieve good performance. However, these models do not give a good indication as to when a drive will fail, only predicting that it will fail. To this end, we propose a new notion of a drive's health degree based on the remaining working time of hard drive before actual failure...
Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of...
The load curve presents certain randomness for reasons such as human social activities, the load of electric vehicle charging and discharging and so on, which covers up the regularity of load sequence. This paper proposes an approach which can restore the original feature of the loads. In this approach, firstly, the bad data is excluded; Secondly, the characteristics of the time series are extracted...
We propose a method to try to model fashionable dresses in this paper. We first discover common visual patterns that appear in dress images using a human-in-the-loop, active clustering approach. A fashionable dress is expected to contain certain visual patterns which make it fashionable. An approach is proposed to jointly identify fashionable visual patterns and learn a discriminative fashion classifier...
Most of the modern hard disk drives support Self-Monitoring, Analysis and Reporting Technology (SMART), which can monitor internal attributes of individual drives and predict impending drive failures by a thresholding method. As the prediction performance of the thresholding algorithm is disappointing, some researchers explored various statistical and machine learning methods for predicting drive...
In nowadays, fault diagnosis method for analog circuit based on support vector machines, has become a hot topic in research field of fault diagnosis. However, in practical application of this method, the imbalanced problem occurred in fault sample dataset has greatly influenced its effectiveness. To remedy this problem, this paper proposed an improved Support Vector Machines method based on biased...
Two methods to efficiently train kernelized support vector machines are introduced. Both of them apply stochastic gradient descent in the primal space. Different from previous fast stochastic kernel machines method [9] which drops old support vectors directly, one of the algorithms exploits the efficient representation of the histogram intersection kernel, the other one approximates the discarded...
E-mail is a major revolution taking place over traditional communication systems due to its convenient, economical, fast, and easy to use nature. A major bottleneck in electronic communications is the enormous dissemination of unwanted, harmful emails known as spam emails. In this paper, a novel spam filtering framework (NSFF) is proposed, which is based on particle swarm optimization, fuzzy logic...
Internet e-mails have become a common medium of communication for nearly every one. With the fast growing, spam interferes with valid email, and bothers users. This paper proposes a new fuzzy adaptive multi-population genetic algorithm (FAMGA), in order to automatically find the best feature subset to classify spam e-mails. FAMGA consists of multiple subpopulations, and each population runs independently...
In the conventional regularized learning, training time increases as the training set expands. Recent work on L2 linear SVM challenges this common sense by proposing the inverse time dependency on the training set size. In this paper, we first put forward a Primal Gradient Solver (PGS) to effectively solve the convex regularized learning problem. This solver is based on the stochastic gradient descent...
It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the support vector machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/?? dependency in complexity to obtain a solution...
Face image based age categorization is an approach to classify face images into one of several pre-defined age-groups. It is challenging because the aging variation is specific to a given individual and is determined by not only the person's gene, but also by many external factors, such as exposure, weather conditions (e.g. ambient humidity), health, gender, living style and living location. Age categorization...
Measuring image similarity is a central topic in computer vision. In this paper, we learn similarity from Flickr groups and use it to organize photos. Two images are similar if they are likely to belong to the same Flickr groups. Our approach is enabled by a fast Stochastic Intersection Kernel MAchine (SIKMA) training algorithm, which we propose. This proposed training method will be useful for many...
We present a method to learn visual attributes (eg."red", "metal", "spotted") and object classes (eg. "car", "dress", "umbrella") together. We assume images are labeled with category, but not location, of an instance. We estimate models with an iterative procedure: the current model is used to produce a saliency score, which, together with...
Recently, learning to rank technique has attracted much attention. However, the lack of labeled training data seriously limits its application in real-world tasks. In this paper, we propose to break this bottleneck by considering the cross-domain ldquotransfer of rank learningrdquo problem. Simultaneously, we propose a novel algorithm called TransRank, which can effectively utilize the labeled data...
We describe a method to retrieve images found on Web pages with specified object class labels, using an analysis of text around the image and of image appearance. Our method determines whether an object is both described in text and appears in a image using a discriminative image model and a generative text model. Our models are learnt by exploiting established online knowledge resources (Wikipedia...
With very low extra computational cost, the entire solution path can be computed for various learning algorithms like support vector classification (SVC) and support vector regression (SVR). In this paper, we extend this promising approach to semi-supervised learning algorithms. In particular, we consider finding the solution path for the Laplacian support vector machine (LapSVM) which is a semi-supervised...
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