Quadratic optimization fine tuning for the Support Vector Machines learning phase

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush–Kuhn–Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.     

BiELL: A bisection ELLPACK-based storage format for optimizing SpMV on GPUs

Sparse matrix–vector multiplication (SpMV) is one of the most important high level operations for basic linear algebra. Nowadays, the GPU has evolved into a highly parallel coprocessor which is suited to compute-intensive, highly parallel computation. Achieving high performance of SpMV on GPUs is relatively challenging, especially when the matrix has no specific structure. For these general sparse matrices, a new data structure based on the bisection ELLPACK format, BiELL, is designed to realize the load balance better, and thus improve the performance of the SpMV. Besides, based on the same idea of JAD format, the BiJAD format can be obtained. Experimental results on various matrices show that the BiELL and BiJAD formats perform better than other similar formats, especially when the number of non-zero elements per row varies a lot.     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging

Early and accurate diagnosis of Parkinson’s disease (PD) is important for early management, proper prognostication and for initiating neuroprotective therapies once they become available. Recent neuroimaging techniques such as dopaminergic imaging using single photon emission computed tomography (SPECT) with ¹²³I-Ioflupane (DaTSCAN) have shown to detect even early stages of the disease. In this paper, we use the striatal binding ratio (SBR) values that are calculated from the ¹²³I-Ioflupane SPECT scans (as obtained from the Parkinson’s progression markers initiative (PPMI) database) for developing automatic classification and prediction/prognostic models for early PD. We used support vector machine (SVM) and logistic regression in the model building process. We observe that the SVM classifier with RBF kernel produced a high accuracy of more than 96% in classifying subjects into early PD and healthy normal; and the logistic model for estimating the risk of PD also produced high degree of fitting with statistical significance indicating its usefulness in PD risk estimation. Hence, we infer that such models have the potential to aid the clinicians in the PD diagnostic process.     

灰色—支持向量机的空调负荷预测

对空调负荷进行准确预测不仅对空调优化控制的意义重大,而且也是实现空调经济运行与节能的关键所在。为了提高建筑空调负荷的预测精度,在分析灰色模型和支持向量机建模特点基础上提出了一种空调负荷组合预测算法。该方法综合了灰色建模计算过程简单以及支持向量机自学习和泛化能力强的优点,能够更加有效地利用样本数据的有效信息,提高模型预测精度。首先,通过灰色建模过程弱化了样本数据的随机因素。然后,对灰色模型输出进行归一化处理及数据重构,以作为支持向量机的输入。最后,通过支持向量机模型的预测得到最终预测结果。将本文所提出的方法应用于福州一栋办公建筑的逐时空调负荷预测中,并与灰色模型及支持向量机模型作比较,证明了组合模型的预测值与实际运行值拟合度最高,平均绝对误差比灰色模型和支持向量机模型分别降低了47.84%和17.39%。该组合预测模型具有较高的预测精度和更好的泛化能力,具有较强的可行性和实用性。     

融合Hu矩与BoF-SURF支持向量机的手势识别

基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果, 但是计算复杂度高、实时性较差。为此, 提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征, 提高了实时性, 并引入Hu矩描述手势全局特征, 进一步提高识别率。实验结果表明, 算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。     

高速列车转向架故障信号的聚合经验模态分解和 模糊熵特征分析

为了对高速列车转向架关键部件进行状态监测,利用转向架故障振动信号的特点,提出了一种结合聚合经验模态分解和模糊熵的特征提取方法.对故障信号进行聚合经验模态分解,得到一系列具有不同物理意义的简单成分信号,采用相关分析法选取最能反映原信号特征的本征模态函数.对这些本征模态函数和原信号分别计算模糊熵值构成多尺度复杂性度量的特征向量,输入最小二乘支持向量机中进行分类识别,与模糊熵特征相比得到了更好的识别效果,证明了算法的有效性.     

隐目标回归算法设计研究

对常用的回归方法进行研究.此类方法虽然几何解释明确、易于求解,但均须事先确定(或假定)变量间的因果关系,再考虑建模,在实际应用中,对于很难确定变量的因果关系的问题,如物联网数据分析,上述方法就会失效.为此,提出一种无需假定因变量的隐目标回归方法.该方法易于核化,可以推广到非线性回归问题.通过人工数据和国际标准数据集上的实验验证了所提算法的有效性.