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Data-parallel architectures must provide efficient support for complex control-flow constructs to support sophisticated applications coded in modern single-program multiple-data languages. As these architectures have wide data paths that process a single instruction across parallel threads, a mechanism is needed to track and sequence threads as they traverse potentially divergent control paths through...
This paper employs sparse Bayesian approach to enable the Probabilistic Classification Vector Machine (PCVM) to select a relevant subset of features. Because of probabilistic outputs and the ability to automatically optimize the regularization items, the sparse Bayesian framework has shown great advantages in real-world applications. However, the Gaussian priors that introduce the same prior to different...
In many document classification problems, sets of people will be associated with the document. These sets might include document authors, or people who have read the document, or the sender of an electronic message, or the recipients of the message, or those carbon copied, or those blind carbon copied. It is obvious that these sets of people can constitute important information that can help to classify...
In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series...
This paper presents a new concept of assessing image quality. It is based on support vector regression (SVR) fusion. Despite the variety of the proposed IQM measures, no efficient and sufficient measure gives good performance over different distortions. Motivated by this problem, a new measure for No reference Image Quality Assessment Based on SVR Fusion (NR BSVRF) is constituted. First, five recent...
Gender classification can play a significant role in security and surveillance system. It aids in identification of a person by recognizing its gender (male/female) from the face image only. Extracting discriminate features for male and female is a fundamental and challenging problem in the field of computer vision. In this manuscript, a combination of Approximation Face Image (AFI) with Principal...
We explore the concept of dictionary learning and sparse coding applied to audio spectrograms. First, we statistically generate a dictionary of feature vectors by sampling many columns of input spectrograms. Then, using ℓ1-regularized least-squares optimization, we transform the columns of the spectrogram into sparse coefficient vectors. Hence, the learned dictionary column features act as an overcomplete...
This paper shows a work done under Affective Computing umbrella and in the field of emotion recognition. The paper explores the anatomy of a human face and builds the classification model based on it. The anatomical information of face is used to locate several points on the face and to extract the features. The features are in form of distance vectors which can be of specific person or group of persons...
In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 letters of the English alphabet in American Sign Language...
Automatic recognition of human emotional states is an important task for efficient human-machine communication. Most of existing works focus on the recognition of emotional states using audio signals alone, visual signals alone, or both. Here we propose empirical methods for feature extraction and classifier optimization that consider the temporal aspects of audio signals and introduce our framework...
In recent year's availability of economical image capturing devices in low cost products like mobile phones has led a significant attention of researchers to the problem of recognizing text in images. Recognition of scene text is a challenging problem compared to the recognition of printed documents. In this work a novel approach is proposed to recognize text in complex background natural scene, word...
Last few decades, multilevel converter topologies have gained a considerable attention. The multilevel converter designs promise a lot of advantages in high power applications, over the conventional converter design such as: lower THD (Total Harmonic Distortion) higher operational voltage ranges, lighter and cheaper topologies. However, due to its higher complexity, also the control algorithms are...
In recent years, many vehicle detection algorithms have been proposed. However, a lot of challenges still remain. Local Binary Pattern (LBP) is one of the most popular texture descriptors which has shown its superiority in face recognition and pedestrian detection. But the original LBP pattern is sensitive to noise especially in flat region where gray levels change rarely. To solve this problem, Local...
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks...
We propose a novel machine learning model for classification problems, Deep Twin Support Vector Machine (DTWSVM), which combines TWSVM with deep learning ideas. TWSVM is a successful algorithm for classification problems which seeks two nonparallel hyper planes to make each hyper plane close to one class and far from the other as much as possible. And Deep Learning (DL) models have shown good ability...
Credit rating prediction using clustering algorithms has become more and more important in the financial literature. Expanding the ideas of [4] and [5], we propose an approach to generate models for automated credit rating prediction based on support vector domain description (SVDD) and linear regression (LR). The models include the prediction for sovereign and corporate bonds. Another advantage is,...
In this paper, we present the ATM (Awesome Translation Machine), which translates handwriting texts in English into Chinese, and then provides its pronunciations in both the two languages. Specifically, two types of the databases that contain characters and sentences for training the ATM are constructed. Various signal processing techniques are employed sequentially for processing and analyzing the...
For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes...
In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
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