Learning with Kernels

Schölkopf, Bernhard; Smola, Alexander J.

doi:10.7551/mitpress/4175.001.0001

Cited by 3,594 publications

(981 citation statements)

References 16 publications

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“…SVR, an extension of Support Vector Machines (SVM), was proposed by Drucker et al [32]. SVR pursues the best trade-off between the model's Empirical Error and the model complexity [33]. This compromise is achieved by constraining SVR regression function f(,) to the hyperplanes function class, and employing a margin, also called insensitive tube, around the hyperplane.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Wind speed forecasting for wind farms: A method based on support vector regression

2016

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a b s t r a c tIn this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.

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Section: Support Vector Regressionmentioning

confidence: 99%

Wind speed forecasting for wind farms: A method based on support vector regression

2016

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“…Examination of the training samples showed it was feasible to precise the RBF parameter and weighting factors. Data base image analysis was classified by using the existing SVM tools of MATLAB, which was used by Scholkopf and Smola to perform classification [19]. Lung nodules are detected by means of specificities and accuracy values.…”

Section: Resultsmentioning

confidence: 99%

Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier

Manikandan¹,

Bharathi²

2016

J Med Syst

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An effective fuzzy auto-seed cluster means morphological algorithm developed in this work to segment the lung nodules from the consecutive slices of Computer Tomography (CT) images to detect the lung cancer. The initial cluster values were chosen automatically by averaging the minimum and maximum pixel values in each row of an image. The area and eccentricity features were used to eliminate the line like structure and very tiny clusters less than 3 mm in size. The change in centroid analysis was carried out to eliminate the blood vessels. The tissue clusters whose centroid varies much in consecutive slices must be blood vessels. After eliminating the blood vessels, the co-occurrence matrix based texture features contrast, homogeneity and auto correlation were computed on the remaining nodules from the consecutive CT slices to discriminate the calcifications. The extracted centroid shift and texture features were used as the inputs to the Support Vector Machine (SVM) kernel classifier in order to classify the real malignant nodules. This work was carried out on 56 malignant (cancerous) cases and 50 normal cases (with lung infections), which had a total of 56 malignant nodules and 745 benign nodules. Out of these, 60 % of subjects (34 cancerous & 30 non-cancerous) were used for training. The remaining 40 % subjects (22 cancerous & 20 non-cancerous) were used for testing. This work produced a good sensitivity, specificity and accuracy of 100 %, 93 % and 94 %, respectively. The False Positive (FP) per patient was calculated as 0.38.

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“…We normalize the spectral and spatial features by min-max method, then both of them are concatenated to get the total 366 dimensional feature set which is used as the input of the SVM classifier. In this paper, we use SVM (Boser et al 1992;Schölkopf and Smola 2002) as the classifier for it is simple to implement. To develop the SVM classifiers, we consider a training set {x i , y i } N i=1 , where x i denotes the input feature vector and y i denotes the target output.…”

Section: Feature Fusion and The Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

Fake modern Chinese painting identification based on spectral–spatial feature fusion on hyperspectral image

Wang

Zhang

et al. 2016

Multidim Syst Sign Process

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Chinese painting is famous and valuable for special painting materials, skills and final art effects used, yet this has resulted in many fake paintings being produced. Those fake paintings were normally made by using modern high resolution scanning and printing technology, thus it is very hard to identify the fake ones by human vision. To address this challenging problem, in this paper, a hyperspectral image based features fusion method is proposed. Firstly, we scan Chinese paintings using a visual band hyperspectral camera with the spectral frequency ranging from 400 to 900 nm. Then, the spectral and spatial features are extracted respectively by using the principal component analysis and a convolution neural network. Finally, we fuse these two kinds of features and input the feature set into a support vector machines for classification. All samples of real and fake paintings are obtained from local Chinese painting organization. The experimental result shows the effectiveness of the proposed method with an accuracy achieved of 84.6 %, which is significantly higher than other approaches where only spectral or spatial feature is used.

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Learning with Kernels

Cited by 3,594 publications

References 16 publications

Wind speed forecasting for wind farms: A method based on support vector regression

Wind speed forecasting for wind farms: A method based on support vector regression

Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier

Fake modern Chinese painting identification based on spectral–spatial feature fusion on hyperspectral image

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