Search results for "SVM"

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…

Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors

2019

BACKGROUND: Incisional hernia is one of the most common complications after abdominal surgery with an incidence rate of 11 to 20% post laparotomy. Many different factors can be considered as risk factors of incisional hernia recurrence. The aim of this study is to confirm and to validate the incisional hernia recurrence risk factors and to identify and to validate new ones. METHODS: In the period from July 2007 to July 2017, 154 patients were selected and subjected to incisional hernia repair. The surgical operations were conducted under general anaesthesia. Patients received antibiotic prophylaxis when indicated, according to the hospital prophylaxis scheme. Inclusion criteria of the study…

Generalized PWM-VSI Control Algorithm Based on a Universal Duty-Cycle Expression: Theoretical Analysis, Simulation Results, and Experimental Validati…

2007

This paper presents a new approach in realizing various carrier-based pulsewidth-modulation techniques by a generalized control algorithm, which is referred to as the universal control algorithm and is obtained via unequal sharing of null states. The flexibility of such an approach allows one to easily and quickly control two-level inverters. Furthermore, this approach may be also extended with few changes to the control of multilevel inverters. The algorithm that is presented here for two-level voltage-source inverters (VSIs) also obtains efficient detection and management of both the linear and overmodulation ranges. In the overmodulation range, which is treated by using the alpha-beta co…

DEVELOPMENT AND IMPLEMENTATION OF MACHINE LEARNING METHODS FOR THE IIF IMAGES ANALYSIS

2021

Learning User's Confidence for Active Learning

2013

In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing the effect of resolution is such a …

A survey of active learning algorithms for supervised remote sensing image classification

2011

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active …

Talent identification in soccer using a one-class support vector machine

2019

Abstract Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support ve…

2014

This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original data or refer to other algorithms, making the classification problem simple to handle. In order to…

A method based on texture feature and edge detection for people counting in a crowded area

2019

We propose a population counting method for feature fusion and edge detection.The image is extracted from multiple information sources to estimate the count by image feature extraction and texture feature analysis, as well as for crowd head edge detection. We count people in high-density still images.For example, in the city bus station, subway. Our method uses a still image taken by the camera to estimate the count in the crowd density image, using multiple sources of information, namely: HOG, LBP, CANNY, these sources provides separate estimates of the number of counts and other statistical measures, through the support vector Machine SVM, classification, and regression analysis to obtain…

Learning the relevant image features with multiple kernels

2009

This paper proposes to learn the relevant features of remote sensing images for automatic spatio-spectral classification with the automatic optimization of multiple kernels. The method consists of building dedicated kernels for different sets of bands, contextual or textural features. The optimal linear combination of kernels is optimized through gradient descent on the support vector machine (SVM) objective function. Since a na¨ive implementation is computationally demanding, we propose an efficient model selection procedure based on kernel alignment. The result is a weight — learned from the data — for each kernel where both relevant and meaningless image features emerge after training. E…