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Showing 1-25 of 11,683 resultsfor "Index Terms":Final Vector
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Discriminative Vector for Spoken Language Recognition
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
Year: 2007 | Conference Paper |
Cited by: Papers (4)
We propose a language recognition system based on discriminative vectors, in which parallel phone recognizers serve as the voice tokenization front-end followed by vector space modeling that effectively vectorizes phonotactic features, and the final classification is carried out based on the discriminative vectors. We design an ensemble of discriminative binary classifiers. The output values of th...Show More
Vectors are massively used in many domains. Several techniques have been proposed for comparing two vectors, but they only perform the comparison according to the exact values of the vector components. Additionally, existing techniques used for comparing two vectors having different dimensions are limited by many factors. Furthermore, the problem of comparing two finite sets of vectors has not yet...Show More
Fusion of Support Vector Machines for Classification of Multisensor Data
The classification of multisensor data sets, consisting of multitemporal synthetic aperture radar data and optical imagery, is addressed. The concept is based on the decision fusion of different outputs. Each data source is treated separately and classified by a support vector machine (SVM). Instead of fusing the final classification outputs (i.e., land cover classes), the original outputs of each...Show More
Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features
Roneel V. Sharan;Udantha R. Abeyratne;Vinayak R. Swarnkar;Paul Porter
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2017 | Conference Paper |
Cited by: Papers (10)
This paper aims to diagnose croup in children using cough sound signal classification. It proposes the use of a time-frequency image-based feature, referred as the cochleagram image feature (CIF). Unlike the conventional spectrogram image, the cochleagram utilizes a gammatone filter which models the frequency selectivity property of the human cochlea. This helps reveal more spectral information in...Show More
Tibetan Text Classification Based on the Feature of Position Weight
2013 International Conference on Asian Language Processing
Year: 2013 | Conference Paper |
Cited by: Papers (13)
Based on the study of Tibetan characters and grammar, this paper has done research on Tibetan in the text categorization weight algorithm based on the vector space model. Comprehensively considering the position information of Tibetan which presented in the text, the paper has proposed an improved TF-IDF weighting algorithm. In the process, it has adopted χ2 (CHI) statistical methods for features ...Show More
A Leaf Recognition Approach to Plant Classification Using Machine Learning
NAECON 2018 - IEEE National Aerospace and Electronics Conference
Year: 2018 | Conference Paper |
Cited by: Papers (16)
The identification of plants is a very important component of workflows in plant ecological research. This paper presents an automated leaf recognition method for plant identification. The proposed technique is simple and computationally efficient. It is based on a combination of two types of texture features, named Bag-of-features (BOF) and Local Binary Pattern (LBP). These features are utilized ...Show More
A Speed-Up Scheme Based on Multiple-Instance Pruning for Pedestrian Detection Using a Support Vector Machine
In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps. Fir...Show More
This paper presents an efficient pedestrian detection by Bin-interleaved Histogram of Oriented Gradients (Bi-HOG) for automotive applications. The state-of-art feature named HOG [5] is adopted as the basic feature. We arrange alternately even-bin cells and odd-bin cells in one block and then extract the only even-bin feature elements for even-bin cells and the only odd-bin feature elements for odd...Show More
Rapid human action recognition in H.264/AVC compressed domain for video surveillance
2013 Visual Communications and Image Processing (VCIP)
Year: 2013 | Conference Paper |
Cited by: Papers (3)
This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than ...Show More
Transformer Fault Diagnosis Based on Improved SVM Model
2009 Fifth International Conference on Natural Computation
Year: 2009 | Conference Paper |
Cited by: Papers (1)
This paper proposes an improved SVM method in order to improve the speed of classification when SVM treats with the large training set. Firstly, using RS theory to eliminate redundant information of the large original training data set, secondly, utilizing the idea of probabilities, train an initial classifier with a small training set, and prune the large training set with the initial classifier ...Show More
Example-Based Super-Resolution Land Cover Mapping Using Support Vector Regression
Super-resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the ...Show More
SVM-Based Data Editing for Enhanced One-Class Classification of Remotely Sensed Imagery
This paper studies a specific one-class classification problem where the training data are corrupted by significant outliers. Specifically, we are interested in the one-class support vector machine (OCSVM) approach that normally requires good training data. However, perfect training data are usually hard to obtain in most real-world applications due to the inherent data variability and uncertainty...Show More
A parallel SVM training algorithm on large-scale classification problems
2005 International Conference on Machine Learning and Cybernetics
Year: 2005 | Conference Paper |
Support vector machine (SVM) has become a popular classification tool but the main disadvantages of SVM algorithms are their large memory requirement and computation time to deal with very large datasets. To speed up the process of training SVM, parallel methods have been proposed by splitting the problem into smaller subsets and training a network to assign samples of different subsets. A paralle...Show More
Face Recognition Based on the Probability Support Vector Machines
2008 International Conference on Computer Science and Software Engineering
Year: 2008 | Conference Paper |
Cited by: Papers (1)
Face recognition is very important in intelligent meeting scenario. An approach to face recognition based on the probability support vector machines is proposed in this paper. In this approach, an approach of the posterior probability output of multi-class SVM is modeled. One-against-one multi-class SVMs with probability output are chosen as the classifiers for face recognition. Considering the re...Show More
Particle Swarm Optimization Based Support Vector Machines in Motion Imagery
2023 3rd International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM)
Year: 2023 | Conference Paper |
In order to improve the classification recognition rate, this paper carries out a theoretical analysis of the support vector machine (SVM), in order to find the optimal parameters C, g, the particle swarm algorithm optimized support vector machine (PSO-SVM) is introduced to carry out parameter optimization, and for the shortcomings of the PSO-SVM, the optimal classifier, i.e., Adaptive Particle Sw...Show More
Support Vector Machines With Constraints for Sparsity in the Primal Parameters
Vanessa Gomez-Verdejo;Manel Martinez-Ramon;Jerónimo Arenas-Garcia;Miguel Lazaro-Gredilla;Harold Molina-Bulla
This paper introduces a new support vector machine (SVM) formulation to obtain sparse solutions in the primal SVM parameters, providing a new method for feature selection based on SVMs. This new approach includes additional constraints to the classical ones that drop the weights associated to those features that are likely to be irrelevant. A ν-SVM formulation has been used, where ν indicates the ...Show More
Analysis and control of the 3- and 5-phase voltage source inverters using the averaged output voltage method
2009 International Conference on Power Electronics and Drive Systems (PEDS)
Year: 2009 | Conference Paper |
A systematic analysis for the multi-phase voltage source inverter (VSI) is illustrated in this paper. By applying the singular value decomposition, the instantaneous output voltage of VSI can be categorized into two complementary groups with the same magnitude and different sign to each other. The switching strategy for minimum conduction time which yields the maximum linear modulation index is pr...Show More
Student performance prediction using Support Vector Machine and K-Nearest Neighbor
Huda Al-Shehri;Amani Al-Qarni;Leena Al-Saati;Arwa Batoaq;Haifa Badukhen;Saleh Alrashed;Jamal Alhiyafi;Sunday O. Olatunji
2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)
Year: 2017 | Conference Paper |
Cited by: Papers (91)
This work presented two prediction models for the estimation of student's performance in final examination. The work made use of the popular dataset provided by the University of Minho in Portugal, which relate to the performance in math subject and it consists of 395 data samples. Forecasting the performance of students can be useful in taking early precautions, instant actions, or selecting a st...Show More
An iterative segmentation algorithm of SAR image based on support vector machine
2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar
Year: 2009 | Conference Paper |
Cited by: Papers (2)
In this paper, an iterative algorithm, which is based on support vector machine (SVM), is proposed for synthetic aperture radar (SAR) image segmentation. The proposed method considers the SAR image segmentation as the pixel classification. The pixels of the previous segmented image are regarded as the training samples for SVM, which is used to re-segment the image. These iterations are repeated un...Show More
Lying human activity recognition based on shape characteristics
Amin Hakimi Rad;Nasrollah Moghadam Charkari;Jalal A. Nasiri;Hamid Reza Shayegh Broojeni
2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE)
Year: 2012 | Conference Paper |
Cited by: Papers (2)
This paper proposes a markerless video analytic system for quantifying body parts movement while lying. These movements include: hand, leg, both hand & leg and turning to left or right movements. Combination of pixel intensity and area difference of both segmented and the whole parts of each silhouette compared with the following silhouettes would provide a useful cue for detection of different bo...Show More
Object recognition based on Gabor wavelet and SVM
2015 IEEE International Conference on Information and Automation
Year: 2015 | Conference Paper |
Cited by: Papers (1)
An object recognition method based on Gabor wavelet and SVM is proposed in this paper. First features of the object are extracted by using Gabor wavelet, and then the dimensions of the Gabor features are reduced with Principal Component Analysis, and finally classification is performed with Support Vector Machine. And this method is applied to the Columbia image library COIL-20 for experiments. Co...Show More
Vehicle recognition model based on improved CNN-SVM
2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
In order to solve the problem of single feature information extraction, low recognition accuracy, and low efficiency of traditional car model recognition methods, convolutional neural networks are introduced into the target recognition problem, and their clear and efficient generalization capabilities are used to complete the feature learning of models. Two aspects of frame structure design and in...Show More
Hyperspectral data classification using RVM with pre-segmentation and RANSAC
2007 IEEE International Geoscience and Remote Sensing Symposium
Year: 2007 | Conference Paper |
Cited by: Papers (3)
Relevance vector machines (RVMs) and support vector machines (SVMs) are known to outperform classical supervised classification algorithms. RVMs have some advantages compared to SVMs, the most important being more sparsity. This paper presents hyperspectral image classification based on relevance vector machines with two different unsupervised segmentation methods as well as RANSAC (RANdom SAmple ...Show More
A boosting method based on SVM for relevance feedback in content-based 3D model retrieval
The 2nd International Conference on Software Engineering and Data Mining
Year: 2010 | Conference Paper |
The technique of relevance feedback has been introduced to content-based 3D model retrieval. Support Vector Machine as a learner is one of the classical approaches in relevance feedback. And the Boosting method, as one of the ensemble methods, can establish a strong leaner by combing the component learners. In this paper, a novel relevance feedback mechanism, which makes use of the main idea of bo...Show More
Final sulfur content prediction model in hot metal desulphurization process based on IEA-SVM
2011 Chinese Control and Decision Conference (CCDC)
Year: 2011 | Conference Paper |
Cited by: Papers (3)
Hot metal desulphurization process is a complex and nonlinear process, final sulfur content in hot metal is difficult to measurement on-line. In order to predict the final sulfur content accurately, a model based on support vector machine (SVM) was proposed. The model cleared the outlier from modeling data through robust regression method and cleared the inconsistent data through rough set theory,...Show More
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