[PDF] Top 20 Choosing Multiple Parameters for Support Vector Machines
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Choosing Multiple Parameters for Support Vector Machines
... We will examine how the accuracy of the estimates influences the whole procedure of finding optimal parameters. In particular we will show that what really matters is how variations of the estimates relate to ... See full document
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Clustering Via Supervised Support Vector Machines
... The fitness of the cluster identified by the SVM-Relabeler algorithm is modeled using a supervised measure, purity (see section 3.3) and an unsupervised measure, Kernel SSE, which is the cohesion of the clusters. In ... See full document
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Areas categorization by operating Support Vector Machines
... In order to employ the SVM approach for object- based image analysis, it is essential to accomplish a subdivision of the image. The SEGM procedure was chosen to accomplish subdivision at many scales [12] and to yield ... See full document
9
Support vector machines with adaptive Lq penalty
... to shrink small |w|’s to exact zeros and hence selects important variables. As pointed out by Theorem 2 in Knight and Fu (2000), when q > 1 the amount of shrinkage towards zero increases with the magnitude of the ... See full document
24
Quadratic Surface Support Vector Machines with Applications.
... rest” method based on the soft QSSVM model and the training data set, we first generate the parameters of the three decision functions. Then the three decision functions are used to classify the total 150 points ... See full document
113
Extracting Important Sentences with Support Vector Machines
... I. Mani and E. Bloedorn. 1998. Machine Learn- ing of Generic and User-Focused Summariza- tion. Proc. of the 15th AAAI, pages 821–826. C. Nobata, S. Sekine, M. Murata, K. Uchimoto, M. Utiyama, and H. Isahara. 2001. ... See full document
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A Tutorial on Support Vector Machines for Pattern Recognition
... by choosing that σ which minimizes D 2 /M 2 ? Figure 14 shows a series of experiments done on 28x28 NIST digit data, with 10,000 training points and 60,000 test ... See full document
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A Hierarchy of Support Vector Machines for Pattern Detection
... scene parsing. For some problems, dedicating a single classifier to each hypothesis, or a cascade (linear chain) of classifiers to a small subset of hypotheses (see Section 2), and then training with existing methodology ... See full document
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Consensus-Based Distributed Support Vector Machines
... dimensional grid of L points as before. A total of 500 Monte Carlo runs were performed. Clearly, the asymptotic performance of MoM-NDSVM rapidly outperforms the average performance of a locally-trained SVM and closely ... See full document
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Laplacian Support Vector Machines Trained in the Primal
... All presented results has been obtained by averaging them on different splits of the available data. In particular, a 4-fold cross-validation has been performed, randomizing the fold generation process for 3 times, for a ... See full document
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Support Vector Machines for Design Space Exploration
... A variety of design of experiment (DoE) strategies is available to reach this goal [3]. The most common ap- proaches focus on statistical aspects and try to place the measurements in a way that the variance error is ... See full document
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Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes
... (ANN), support vector machines (SVMs), and multiple logistic regression (MLR) models were applied, using demographic, anthropometric, and biochemical characteristics, on a sample of 9528 ... See full document
9
Africans, Cherokees, and the ABCFM Missionaries in the Nineteenth Century: An Unusual Story of Redemption
... sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure ...with multiple windows to form the protein data ... See full document
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STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES
... 5.4 Looped-Rating Curve Modelling The SVMs approach was also employed to predict the discharge using a hypothetical data set in order to model a looped-rating curve. Two different data sets for rising and falling stages ... See full document
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Spatio-temporal avalanche forecasting with Support Vector Machines
... of parameters has increased through technological advances in sensor networks and automated environmental monitoring (Hart and Martinez, 2006), we can expect data-driven models to become increasingly ... See full document
16
Infinite ensemble learning with support vector machines
... In this section, we introduce two important properties of the SVM-based framework. First we show that the framework allows us to embed multiple base learning models together with a simple summation over the ... See full document
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Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... Nonlinear SVR models: After the final set of variables is selected, we employ Algorithm 2 in the appendix to construct the nonlinear SVR model. Nonlinear models were constructed based on T = 10 partitions and then ... See full document
15
Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment
... for multiple indices of diffusion within the white matter voxels of each ...with support vector machines (SVMs) to classify control and MCI ... See full document
11
Traffic Flow Condition Classification for Short Sections Using Single Microwave Sensor
... ultrasonic, and analog/digital camera technologies [1]. Since the quick and effortless installation is possible only on IL technology, we have used Radio Transmissions Microwave Sensor-based (RTMS) IL detector. We have ... See full document
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A Novel Hyper-parameters Selection Approach for Support Vector Machines to Predict Time Series
... As [5] pointed out, although SVM used for time series prediction span many practical application areas, there appears to be several challenges associated with the use of SVM, among which is the free parameter selection. ... See full document
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