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This paper deals with the lexicon-based approach in document-level and sentence e-level sentiment analysis (SA) in Arabic. We experimented four different lexicons, a translation of Harvard IV-4 Dictionary (HarvardA), translation of the MPQA subjectivity lexicon developed by Pittsburgh University (HRMA) and two different implementation of MPQA. We evaluated all four lexicons with three datasets from...
A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented. The proposed method exploits local geometric data information within the OC-ELM optimization process. It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM...
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training...
In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
In this work, we investigate the problem of predicting gender from still images using human metrology. Since the values of the anthropometric measurements are difficult to be estimated accurately from state-of-the-art computer vision algorithms, ratios of anthropometric measurements were used as features. Additionally, since several measurements will not be available at test time in a real-life scenario,...
In this paper, a new multi-class classification method is proposed and evaluated in the problem of human action recognition in unconstrained environments. The proposed method exploits both the maximum margin property of multi-class Support Vector Machines and Linear Discriminant Analysis-based discrimination. Experiments indicate that by exploiting such discriminant information in a multi-class maximum...
Automatic diagnosis for fetal echocardiography plays an important part in diagnostic aid in the discrimination of congenital heart disease (CHD). Instead of traditional methods analyzing 2D cardiac echo video that need to find the standard view for discrimination, in this paper, we proposed a new system for automatic discrimination of CHD applying 4D original echocardiogram, which avoids the challenging...
In this paper, we formulate a variant of the Support Vector Machine classifier that exploits graph-based discrimination criteria within a multi-class optimization process. We employ two kNN graphs in order to describe intra-class and between-class data relationships. These graph structures are combined in order to form a regularizer which is used in order to regularize the multi-class SVM optimization...
In this paper a variant of the binary Support Vector Machine classifier that exploits intrinsic and penalty graphs in its optimization problem is proposed. We show that the proposed approach is equivalent to a two-step process where the data is firstly mapped to an optimal discriminant space of the input space and, subsequently, the original SVM classifier is applied. Our approach exploits the underlying...
Automatic detection of falls is important for enabling people who are older to safely live independently longer within their homes. Current automated fall detection systems are typically designed using inertial sensors positioned on the body that generate an alert if there is an abrupt change in motion. These inertial sensors provide no information about the context of the person being monitored and...
Automated classification of retinal vessels in fundus images is the first step towards measurement of retinal characteristics that can be used to screen and diagnose vessel abnormalities for cardiovascular and retinal disorders. This paper presents a novel approach to vessel classification to compute the artery/vein ratio (AVR) for all blood vessel segments in the fundus image. The features extracted...
To deal with the problem that traditional satellite remote sensing image change detection methods overestimate changed areas, a context-sensitive similarity based supervised satellite image change detection method was proposed. Both context-sensitive magnitude and direction of change in the vicinity of each pixel by means of local intercept and slope were exploited, and then SVM (support vector machine)...
Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted...
The EMG signals are being used in electronic systems with biofeedback control for tracking and classifying of hand motion. These systems present a challenge in identifying the movement due to the variation of the EMG signals between subjects, therefore different pattern recognition techniques have been implemented to overcome this challenge. In response to the previous problem, the present study compares...
In this paper, a supervised classification algorithm of polarimetric synthetic aperture radar (PolSAR) images based on SVM with training sample optimization is proposed. For the supervised strategy, three main steps are there including feature extraction, design of classifier and training of classifier. Firstly, some features of the PolSAR images are extracted by employing the target decomposition...
Epilepsy is a chronic neurological disorder which occurs due to the recurring evoking of seizure which results due to the abnormal rhythmic discharge of electrical activities of the brain. This fluctuation in the electrical activities of the brain can be analyzed using EEG signal which provides valuable information about the physiological states of the brain. In this paper we propose an efficient...
Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an...
Label-deficient semi-supervised learning is a challenging setting in which there is an abundance of unlabeled data but a dearth of labeled data. We propose a method for applying Gaussian process latent variable models (GPLVM) in a label-deficient setting, a method in which the discriminative GPLVM objective function trains a back-constraining neural network followed by a transformation into a semi-supervised...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
As Twitter offers a fertile ground for expressing different thoughts and opinions, it can be seen as a valuable tool for sentiment analysis. Furthermore, properly identified reviews present a baseline of information as an input to different systems, such as e-learning systems, decision support systems etc. However, the data preprocessing is a crucial step in sentiment analysis, since selecting the...
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