Output Support Vector Research Articles

Studying the Impact of Health Education on Student Knowledge and Behavior through Big Data and Cloud Computing

Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. The application of artificial intelligence and big data to the mental health education practice of college students has a very positive effect on accurately discovering and scientifically solving the mental health problems of college students. In order to combine big data and cloud computing platform organically, this paper introduces an intelligent algorithm based on multi-output support vector regression (MSVR) model and immune clone selection algorithm (ICSA). At the same time, we couple the two to obtain a new intelligent algorithm, namely, immune multiple output support vector regression (ICSA-MSVR) algorithm. Based on the prediction results of health education on students' knowledge and behavior by cloud computing platform, the necessary conditions for three intelligent algorithms to complete the task are summarized. Numerical experimental results show that ICSA-MSVR plays a role in both local search and global search, and is more effective in large-scale cloud computing task scheduling. In addition, in task scheduling, when the task completion time is short, ICSA-MSVR has a lower load imbalance than ICSA and MSVR, which can achieve better load balancing, and the load between virtual machines is closer. Finally, combined with the problems and the needs of students’ health education, suggestions are put forward to deepen the application of technology in students’ mental health education. This approach can provide corresponding ideas and reference methods for improving the scientificity, pertinence, and effectiveness of mental health education.

A new approach for writer verification based on segments of handwritten graphemes

Abstract Traditional literature considers complex biometric sources such as words, letters and signatures for writer verification/identification. In this work the use of small segments of the handwritten stroke for writer verification is proposed. A grapheme is defined as the concatenation of smaller segments or fragments. Two models of grapheme are developed based on the idea that the segments are parts of a circle with or without direction. The average of Gray Level of the Perpendicular Line to the Skeleton and Local Binary Pattern are adopted as descriptors. A database of 3,000 images of 50 writers, with 6 types of segments and with 10 samples per segment has been developed. A binary output support vector machine was applied as classifier. Thus, 50 classifiers were trained using 100 balanced data sets generated using subsampling of the majority class. Experiments are carried out with the proposed models, with an identity verification hit rate of 97 % on average. The positive results of experiments show that it is possible to achieve high precision in writer verification using segment of graphemes.

Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Automated Fundoscopy for Glaucoma Detection and Classifiction

glaucoma is leading chronic eye dieses in the world that leads to vision lost. The main cause of Glaucoma is intrinsic deterioration of the optic nerve which leads high intraocular pressure of the eye. Manually detection of glaucoma is tedious and costly. In our work we are providing automated system for glaucoma detection which is based on fully connected conditional random filed (FC-CRF) model, it works on long and thin structure. Conditional random filed provide a platform for structure prediction. Taking benefit of current results, validating assumption and parameters of our system learned automatically with the help of structured output support vector machine. Our system trained both quantitatively and qualitatively on publically existing data sets: DRIVE, STARE, CHASEDB1 and HRF. Once we obtain segmentation results further classification is done by SVM and K-NN classifier results of our proposed system is analyzed with gold standard labeling provided each data sets in terms of TP,TN,FP and FN. importance of our proposed system is it works for enlarge structure which can provide a platform to other biomedical and biological applications.

Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm

As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

Dynamic Collaborative Tracking

Correlation filter has been demonstrated remarkable success for visual tracking recently. However, most existing methods often face model drift caused by several factors, such as unlimited boundary effect, heavy occlusion, fast motion, and distracter perturbation. To address the issue, this paper proposes a unified dynamic collaborative tracking framework that can perform more flexible and robust position prediction. Specifically, the framework learns the object appearance model by jointly training the objective function with three components: target regression submodule, distracter suppression submodule, and maximum margin relation submodule. The first submodule mainly takes advantage of the circulant structure of training samples to obtain the distinguishing ability between the target and its surrounding background. The second submodule optimizes the label response of the possible distracting region close to zero for reducing the peak value of the confidence map in the distracting region. Inspired by the structure output support vector machines, the third submodule is introduced to utilize the differences between target appearance representation and distracter appearance representation in the discriminative mapping space for alleviating the disturbance of the most possible hard negative samples. In addition, a CUR filter as an assistant detector is embedded to provide effective object candidates for alleviating the model drift problem. Comprehensive experimental results show that the proposed approach achieves the state-of-the-art performance in several public benchmark data sets.

The Classification of EEG Signals with Multi-Domain Fusion Based on D-S Evidence Theory

The classification of electroencephalogram (EEG) signals is a key technique of brain–computer interface (BCI) system. In view of the complexity of EEG signals and the low accuracy in EEG signals recognition, a motor imagery EEG signals classification method with multi-domain fusion based on Dempster–Shafer (D-S) evidence theory is presented in this paper. Firstly, time domain statistics (TDS), autoregressive (AR) model and discrete wavelet transform (DWT) are used to extract features from EEG signals, respectively, and three probabilistic output support vector machine (SVM) classification models are trained based on these three feature sets. Secondly, using the output of each SVM, we construct basic probability assignment (BPA) function and get fusion BPA through D-S evidence theory. Finally, determining the class of test samples based on decision rules. Four databases from BCI competition are employed to evaluate the proposed approach, and the highest classification accuracy reaches 92.83%. Results show that this method acquires higher accuracy and has strong individual adaptability.

Early detection and risk assessment for chronic disease with irregular longitudinal data analysis.

Early detection and risk assessment of complex chronic disease based on longitudinal clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. Disease diagnosis with computer-aided methods has been extensively studied. However, early detection and contemporaneous risk assessment based on partially labeled irregular longitudinal measurements is relatively unexplored. In this paper, we propose a flexible mixed-kernel framework for training a contemporaneous disease risk detector to predict the onset of disease and monitor the disease progression. Moreover, we address the label insufficiency problem by identifying the pattern of disease-induced progression over time with longitudinal data. Our method is based on a Structured Output Support Vector Machine (SOSVM), extended to longitudinal data analysis. Extensive experiments are conducted on several datasets of varying complexity, including the contemporaneous risk assessment with simulated irregular longitudinal data; the identification of the onset of Type 1 Diabetes (T1D) with irregularly sampled longitudinal RNA-Seq gene expression dataset; as well as the monitoring of the drug long-term effects on patients using longitudinal RNA-Seq dataset containing missing time points, demonstrating that our method enhances the accuracy in both early diagnosis and risk estimation with partially labeled irregular longitudinal clinical data.

Structured Output Prediction Using Privileged Information

We study the problem of structured output prediction. Some methods such as structured output support vector machines (SSVM) and conditional random fields (CRFs) are state-of-the-art in dealing with the structured data. However, these classical methods have some limits in scalability because of high memory requirements and slow training speed. Recently, the method joint kernel support estimation (JKSE) has been proposed based on one-class SVM which can be trained efficiently. However, JKSE is not as powerful as those classical methods from the point of prediction performance. To improve the performance of JKSE, we introduce privileged information into it. Learning using privileged information (LUPI) is an advanced machine learning paradigm by taking advantage of some elements of human teaching that are only available at the training stage, not at testing. Motivated by the LUPI, we propose three new models based on JKSE by considering three forms of privileged information. The resulting optimization problems are convex quadratic programming that can be easily solved. Our new models not only persist the advantage of JKSE but also improve its performance. The experimental results show the superiority of new models over the JKSE when solving object detection and multi-class classification problems.

High performance visual tracking with circular and structural operators

Visual tracking algorithms based on structured output support vector machine (SOSVM) have demonstrated excellent performance. However, sampling methods and optimization strategies of SOSVM undesirably increase the computational overloads, which hinder real-time application of these algorithms. Moreover, due to the lack of high-dimensional features and dense training samples, SOSVM-based algorithms are unstable to deal with various challenging scenarios, such as occlusions and scale variations. Recently, visual tracking algorithms based on discriminative correlation filters (DCF), especially the combination of DCF and features from deep convolutional neural networks (CNN), have been successfully applied to visual tracking, and attains surprisingly good performance on recent benchmarks. The success is mainly attributed to two aspects: the circular correlation properties of DCF and the powerful representation capabilities of CNN features. Nevertheless, compared with SOSVM, DCF-based algorithms are restricted to simple ridge regression which has a weaker discriminative ability. In this paper, a novel circular and structural operator tracker (CSOT) is proposed for high performance visual tracking, it not only possesses the powerful discriminative capability of SOSVM but also efficiently inherits the superior computational efficiency of DCF. Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters. Furthermore, an implicit interpolation is applied to convert the multi-resolution feature maps to the continuous domain and make all primal confidence score maps have the same spatial resolution. Then, we exploit an efficient ensemble post-processor based on relative entropy, which can coalesce primal confidence score maps and create an optimal confidence score map for more accurate localization. The target is localized on the peak of the optimal confidence score map. Besides, we introduce a collaborative optimization strategy to update circular and structural operators by iteratively training structural correlation filters, which significantly reduces computational complexity and improves robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance in mean AUC scores of 71.5% and 69.4% on the OTB2013 and OTB2015 benchmarks respectively, and obtains a third-best expected average overlap (EAO) score of 29.8% on the VOT2017 benchmark.

Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

Conventional tracking-by-detection approaches for visual object tracking often assume that the task at hand is a binary foreground-versus-background classification problem, in which the background is a single, generic, and all-inclusive class. In contrast, here we argue that the background appearance, for the most part, possesses a more complicated structure that would benefit from further partitioning into multiple contextual clusters. Our observation is that, although the background class is contemplated to contain a vast intra-class variation, during the tracking process, only a small portion of this diversity is present at the current frame around the foreground object. This observation motivates us to build multiple fine-grained foreground-versus-contextual-cluster models that provide more discriminative classifications, and consequently more robust and accurate foreground object tracking. For each cluster, we employ a structured output support vector machine (SSVM), and in an online manner, we combine the responses of multiple classifiers. To this end, we apply a top-level SSVM that models the tracked foreground object. We show that our refined modeling of the background is better than naively growing the complexity of a single foreground–background classifier, i.e., increasing the number of support vectors that existing approaches rely on, which cause overfitting issues. Our extensive evaluations on large benchmark data sets demonstrate that our tracker consistently outperforms the current state-of-the-art while having comparable computational requirements.

A multiple support vector machine approach to stock index forecasting with mixed frequency sampling

The independent variables commonly used to predict the stock price index usually contain data sampled at different frequencies, and simultaneously, there exist multiple outputs. However, most current researches ignore different frequencies among independent variables and multi-output issues. This paper proposes a multiple output support vector machine unrestricted mixed data sampling (MSVM-UMIDAS) approach – which can achieve multiple results for sequential points simultaneously by applying mixed frequency independent variables. We test the in-sample and out-of-sample performances of MSVM-UMIDAS for stock forecasting in terms of (t−1), (t−2) and (t−3) and then compare the performances of the proposed model with those of other models. The results indicate that our model performs better when assessed by four different measurements. Thus, our proposed model is more realistic in practice and an appropriate tool for multi-output and mixed frequency issues for stock price forecasting.

Automated segmentation and area estimation of neural foramina with boundary regression model

Accurate segmentation and area estimation of neural foramina from both CT and MR images are essential to clinical diagnosis of neural foramina stenosis. Existing clinical routine, relying on physician's purely manual segmentation, becomes very tedious, laborious, and inefficient. Automated segmentation is highly desirable but faces big challenges from diverse boundary, local weak/no boundary, and intra/inter-modality intensity inhomogeneity. In this paper, a novel boundary regression segmentation framework is proposed for fully automated and multi-modal segmentation of neural foramina. It creatively formulates the segmentation task as a boundary regression problem which models a highly nonlinear mapping function from substantially diverse neural foramina images directly to desired object boundaries. By leveraging a seamless combination of multiple output support vector regression (MSVR) and multiple kernel learning (MKL), the proposed framework enables the domain knowledge learning in a holistic fashion which successfully handles the extreme diversity posing a tremendous challenge to conventional segmentation methods. The performance evaluation was conducted on a dataset including 912 MR images and 306 CT images collected from 152 subjects. Experimental results show that the proposed automated segmentation framework is highly consistent with physician with average DSI (dice similarity index) as high as 0.9005 (CT), 0.8984 (MR), 0.8935 (MR+CT) and BD (boundary distance) as low as 0.6393mm (CT), 0.6586mm (MR), 0.6881mm (MR+CT). Based on this accurate automated segmentation, the estimated areas, highly correlated to their independent ground truth, have been achieved with correlation coefficient: 0.9154 (CT) and 0.8789 (MR). Hence, the proposed approach enables an efficient, accurate and convenient tool for clinical diagnosis of neural foramina stenosis.

Coupling Reranking and Structured Output SVM Co-Train for Multitarget Tracking

Most of the previous works for multitarget tracking employ two strategies: global optimization and online state estimation. In general, global methods attempt to prevent local optimization and find the best results given global models. However, in time-critical applications, global optimization has long temporal latency. In contrast, most of the online algorithms obtain the states with greedy estimation, in which the errors are hard to be corrected. In this paper, we combine these two strategies and propose an online tracking approach with short-term storage to correct some local association errors. Based on structured output support vector machine, we propose a new framework with multiple online learners to produce multiple best local linkages, and novel features based on previously generated multiframe associations are designed for reranking of these multiple linkages. The reranking also serves as the appropriate mediator for updating of the online learners by co-train algorithm. The experimental results illustrate the advantage and robustness of this reranking algorithm, and its discrimination to find optimal ones. Comparison with some state-of-the-art methods proves that our proposed method is competitive to global optimal ones and is superior to other online tracking algorithms.

Multi-view facial landmark detector learned by the Structured Output SVM

We propose a real-time multi-view landmark detector based on Deformable Part Models (DPM). The detector is composed of a mixture of tree based DPMs, each component describing landmark configurations in a specific range of viewing angles. The usage of view specific DPMs allows to capture a large range of poses and to deal with the problem of self-occlusions. Parameters of the detector are learned from annotated examples by the Structured Output Support Vector Machines algorithm. The learning objective is directly related to the performance measure used for detector evaluation. The tree based DPM allows to find a globally optimal landmark configuration by the dynamic programming. We propose a coarse-to-fine search strategy which allows real-time processing by the dynamic programming also on high resolution images. Empirical evaluation on “in the wild” images shows that the proposed detector is competitive with the state-of-the-art methods in terms of speed and accuracy yet it keeps the guarantee of finding a globally optimal estimate in contrast to other methods.

Struck: Structured Output Tracking with Kernels.

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.

Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.

Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a “medium-throughput” pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0006-4) contains supplementary material, which is available to authorized users.

General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.

Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

Optimizing different loss functions in multilabel classifications

Multilabel classification (ML) aims to assign a set of labels to an instance. This generalization of multiclass classification yields to the redefinition of loss functions and the learning tasks become harder. The objective of this paper is to gain insights into the relations of optimization aims and some of the most popular performance measures: subset (or 0/1), Hamming, and the example-based F-measure. To make a fair comparison, we implemented three ML learners for optimizing explicitly each one of these measures in a common framework. This can be done considering a subset of labels as a structured output. Then, we use structured output support vector machines tailored to optimize a given loss function. The paper includes an exhaustive experimental comparison. The conclusion is that in most cases, the optimization of the Hamming loss produces the best or competitive scores. This is a practical result since the Hamming loss can be minimized using a bunch of binary classifiers, one for each label separately, and therefore, it is a scalable and fast method to learn ML tasks. Additionally, we observe that in noise-free learning tasks optimizing the subset loss is the best option, but the differences are very small. We have also noticed that the biggest room for improvement can be found when the goal is to optimize an F-measure in noisy learning tasks.