Prediction techniques are an interesting challenge in many areas like weather, banking, and finance, healthcare and so on. They are also becoming a popular subject in networking domain. This topic explores link load prediction of a network using Support Vector Regression and Optimization techniques. Support Vector Regression(SVR) is robust to outliers and can be used to online and adaptive lear...

We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but a continuous measure called sentiment polarity score (sp-score). An sp-score gives a very concise summary of a review and provides more information than binary classification. The difficulty of this task lies in the ...

This paper investigates the use of Local Linear Embedded Regression (LLER) for the quantitative analysis of glucose from near infrared spectra. The performance of the LLER model is evaluated and compared with the regression techniques Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) both with and without pre-processing. The predic...

In this paper, a novel method is proposed to monitor the power system voltage stability using Support Vector Machine (SVM) by implementing real-time data received from the Wide Area Measurement System (WAMS). In this study, the effects of the protection schemes on the voltage magnitude of the buses are considered while they have not been investigated in previous researches. Considering overcurr...

Accurate and reliable prediction of the groundwater level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of groundwater level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector ...

ε-support vector regression (ε-SVR) can be converted into an unconstrained convex and non-smooth quadratic programming problem. It is not solved by the traditional algorithm. In order to solve this non-smooth problem, a class of piecewise smooth functions is introduced to approximate the ε-insensitive loss function of ε-SVR, which generates a ε-piecewise smooth support vector regression (ε-dPWS...

We designed and implemented a wall-following robot that learned appropriate actions from a small set of noisy training examples. We then assessed the effectiveness of this strategy by physically modifying the robot prior to training. Our system used nearest-neighbour classification to make motor command predictions, but we also tested two regression techniques for continuously varying action se...

In [1], with the evidence framework, the almost inversely linear dependency between the optimal parameter r in norm-r support vector regression machine r-SVR and the Gaussian input noise is theoretically derived. When r takes a non-integer value, r-SVR cannot be easily realized using the classical QP optimization method. This correspondence attempts to achieve two goals: (1) The Newton-decent-m...

To study the relationship between chlorophyll-a and environmental variables during spring algal bloom in Xiangxi Bay of Three Gorges Reservoir, the support vector regression (SVR) model was established. In surveys, 11 stations have been investigated and 264 samples were collected weekly from March 4 to May 13 in 2007 and February 16 to May 10 in 2008. The parameters in SVR model were optimized ...