Heat loss evaluation for heating building envelope based on relevance vector machine

In this paper, we present a comparative evaluation of several appearance and shape descriptors in the context of 3D human pose estimation. Among the shape descriptors, we evaluate the Discrete Cosine Transform (DCT) and the Histogram of Shape Context (HoSC) descriptors. The five appearance descriptors that we evaluate are all variants of the Histogram of Oriented Gradients (HOG) descriptor. We evaluate these descriptors quantitatively using the HumanEva-I dataset. We report the performance of the descriptors using the Relevance Vector Machine (RVM) regression and K-nearest neighbor (KNN) regression methods. We found that the appearance descriptor computed at multiple spatial regions gave the best performance when RVM regression was used for pose estimation. The DCT descriptor performed the best when KNN regression was used for pose estimation.

Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric Bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as: y= Dw+s+e where Dw is the relevance vector component, of which D is the kernel function matrix and w is the weight matrix, s is the spiky term and e is the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector, w which gives a more intuitive constraint on the sparsity than the Student’s t-distribution described in the traditional RVM. For the spiky component, s a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.

Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks

Application of relevance vector machine and logistic regression for machine degradation assessment

This article adopts three soft computing techniques including support vector machine (SVM), least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.

We present a probabilistic ranking-driven classifier for the detection of video semantic concept, such as airplane, building, etc. Most existing concept detection systems utilize Support Vector Machines (SVM) to perform the detection and ranking of retrieved video shots. However, the margin maximization principle of SVM does not perform ranking optimization but merely classification error minimization. To tackle this problem, we exploit the sparse Bayesian kernel model, namely the relevance vector machine (RVM), as the classifier for semantic concept detection. Based on automatic relevance determination principle, RVM outputs the posterior probabilistic prediction of the semantic concepts. This inference output is optimal for ranking the target video shots, according to the Probabilistic Ranking Principle. The probability output of RVM on individual uni-modal features also facilitates probabilistic fusion of multi-modal evidences to minimize Bayes risk. We demonstrate both theoretically and empirically that RVM outperforms SVM for video semantic concept detection. The testings on TRECVID 07 dataset show that RVM produces statically significant improvements in MAP scores over the SVM-based methods.

Discriminative training aims at constructing a classifier that is of small scale but has high classification power. One type, Minimum Classification Error (MCE) training, has been used widely in pattern recognition, especially in the speech recognition field. In parallel with this, Relevance Vector Machine (RVM) has attracted many researchers' interest, based on its potential for alleviating the scalability problem of Support Vector Machine (SVM). It has been reported that RVM achieves high classification accuracy with a limited amount of classifier parameters, i.e., relevance vectors. Comparison studies between MCE training and SVM have been done, but not so much between MCE training and RVM. Motivated by this, we conduct theoretical and experimental comparisons of MCE training and RVM. Results show that MCE training is better suited to the development of small-scale but highly discriminative classifiers than its counterpart.

The study of lithium-ion battery management has always been a hot issue in energy field. Predicting the remaining useful life (RUL) helps to know the time of maintenance and replacement in advance so that users can be avoided from unexpected battery fault. In this paper, a RUL prediction method on the basis of a sparse probability model in the Bayesian framework which is Relevance Vector Machine (RVM) is proposed. Hybrid kernel function is introduced to ensure the prediction accuracy. A newly heuristic algorithm Cuckoo Search (CS) is adopted to intelligently determine all the kernel parameters along with the weight of each kernel function. Contrast experiments show that our proposed approach based on hybrid kernel RVM with CS (CS-HKRVM) outperforms the basic machine learning algorithms including single kernel RVM, Back Propagation Neural Network and Support Vector Machine. The hybrid kernel RVM with particle swarm optimization (PSO) is also conducted in order to demonstrate that CS is more effective and accurate than PSO in searching for the optimal solution of prediction model construction.

This article proposes and verifies a novel intelligence approach for modelling forest fire danger, namely ICA-RVM, developed based on Relevance Vector Machine (RVM) and Imperialist Competitive Algorithm (ICA), state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling. RVM is used to establish a prediction model that computes probability of fire danger, whereas ICA is adopted to optimize the prediction model. The tropical forest at Gia Lai province, Central Highland (Vietnam), was used as a case study. Area under the curve (AUC) and statistical measures were used to assess the model performance. The result showed that the proposed model achieves high performances; AUC is 0.842 and 0.793 on the training dataset and the validation dataset, respectively. Compared to two benchmarks, Random Forests and Support Vector Machine, the proposed model performs better. Therefore, the propose ICA-RVM is a valid alternative system for forest fire danger modeling.

Hughes phenomenon is a serious problem in supervised classification of hyperspectral images in particular for small land-cover patches. A solution for this problem through integrating the capabilities of a nonlinear discriminating analysis with relevance vector machine (RVM) is proposed in this paper. It first transforms the hyperdimensional data to a new space with a better class separability. Then, a multiclass RVM classifier processes the transformed data for precise labeling of the classes. The results show that the proposed approach outperforms both RVM as well as support vector machine (SVM), when they are applied to the original hyperdimensional data space. Indeed, it is an advantage for key information detection in the classification context.

As relevance vector machine (RVM) can powerfully manage complexity to regression and classification basing on the concept of probabilistic Bayesian learning framework, it has been widely used in dealing with various recognition problems. In present study we applied RVM as a statistical downscaling method to climate change impact on hydrology and water resources. General circulation models (GCMs) are main tools for study global climate change, however, their simulate results cannot be used directly to evaluate the impact of climate change on hydrology in basin scale for their large and coarse scale. By applying downscaling approach based on RVM, the complex non-linear relationship between the climate factors of GCMs and runoff in basin scale was bridged. The impact of climate change on runoff was assessed by using the established relationship. Comparing with the other two downscaling approach, least support vector machine (LSSVM) and Back propagation neural network (BPNN), the results showed that RVM is suitable for assessing climate change impact on hydrology as rational modeling accuracy and fast modeling speed.

Objective To construct the molecular model and screen the key molecules for evaluation of tumor metastatic potential in nude mice bearing hepatocellular carcinoma (HCC) xenografts.Methods Orthotopic HCC models were established by implantation of human HCC cell line MHCC97H xenografts with high metastatic potential.Thirty-six nude mice bearing HCC were randomized into three groups 14 days post-operation,including palliative resection group (samples A,B),sham operation group (sample C1),and control group (sample C2).Six mice in each group were sacrificed by cervical dislocation 14 days after palliative resection.Oligo Tumor Metastasis Microarray (OHS-028) and GEArray Expression Analysis Suite software were adopted for gene analysis.The methods of support vector machine (SVM),gene significance analysis and gene correlation degree analysis were used to find the markers that could differentiate the metastatic potential of HCC.Gene function net was constructed based on the special gene clustering analysis and multi-dimensional scale.The relevance vector machine (RVM) was used to deal with regression and classification problems.Real-time fluorescence quantitative polymerase chain reaction (FQ-PCR) was applied to detect mRNA expression.Results By the method of support vector machine (SVM),we found the markers composed of 12 genes (BAI1,MTA2,MTA1,SMAD2,GNRH1,CDHS,ITGB3,CHD4,GZMA,ITGA7,CXCR4,and TSHR) could differentiate the metastatic potential of HCC with positive predictive rate of 88.9％ and 93.5％ in the groups with high and low metastatic potential respectively.In addition,it was found that MTSS1,transforming growth factor-β1 (TGF-β1),SMAD2,interleukin-1 β (IL-1 β) and matrix metalloproteinase-7 (MMP-7) were situated in the central position of the gene function net with high metastatic potential,which was confirmed by Real-time PCR.Conclusion The constructed molecule diagnostic model and the key molecules may be used to evaluate metastatic potential of HCC. Key words: Carcinoma, hepatocellular ; Molecular model; Invasion ; Metastasis

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called <i>relevance vectors</i>. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.