[PDF] Top 20 A Study on SMO-type Decomposition Methods for Support Vector Machines
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A Study on SMO-type Decomposition Methods for Support Vector Machines
... Shrinking and caching are two effective techniques to speed up the decomposition methods. Earlier research [18] gives the theoretical foundation of these two techniques, but requires some assumptions. In ... See full document
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A Review Of Machine Learning Techniques And Statistical Models In Anaemia
... this study, the optimal condition for a stable haemoglobin level has to be maintained between the range of 11 to 12 g/dl as recommended, and the concentration of the hemoglobin set above 12 ...and support ... See full document
5
Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines
... We used the 4 ²³ grade USA data in the dataset. 12569 U.S students participated in this study. Students were administered a background questionnaire with questions related to their home background, school ... See full document
10
Areas categorization by operating Support Vector Machines
... These training images are called support vectors. Training images other than support vectors are rejected. This way, not only is a best hyper plane fitted, but also less training samples are efficiently ... See full document
9
The Corporate Financial Forecasting Based on Least Squares Support Vector Machines Methods
... as the problem that how to ascertain the network structure, the over-fitting problem, the local minima problem. These are essentially because the contradic- tions of infinite sample in theory and finite sample in ... See full document
7
Infinite ensemble learning with support vector machines
... or Support Vector Machine (Vapnik ...to study whether infinite ensemble learning, that is, an infinite mixture of hypotheses, could also work ... See full document
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Quadratic Surface Support Vector Machines with Applications.
... By far, almost all SVM research has been focused on using kernels to perform the non- linear classification. As the commonly-used machine learning techniques [104, 112, 113], SVM models with kernels have achieved great ... See full document
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Intelligent Optimization Methods for High Dimensional Data Classification for Support Vector Machines
... These UCI datasets consist of numeric and nominal at- tributes. To guarantee valid results for making predic- tions regarding new data, the dataset is further randomly partitioned into training sets and independent test ... See full document
11
Automatic hand phantom map generation and detection using decomposition support vector machines
... Based on the consideration that the human phantom map detection process is likely gradual and adaptive, we decided to apply active learning as well to the four decompo- sition SVMs. Active learning is able to query the ... See full document
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Krylov Iterative Methods for Support Vector Machines to Classify Galaxy Morphologies
... experimental study on the behavior of four Krylov iterative methods, BiCG, BiCGSTAB, QMR, and ...iterative methods which are applicable to non-Hermitian ...matrix vector products, one ... See full document
15
Characterization of digital medical images utilizing support vector machines
... Feed-forward neural networks with one hidden layer and allowing skip-layer connections directly from the inputs to the output were fitted in this study using the S-Plus pro- gramming language. Because of the ... See full document
9
Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity
... that study lead us to suggest that LDA can be used to identify candidate markers associated with agronomic ...traditional methods of multivariate analysis are ineffective for classification analyses because ... See full document
10
Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension
... Rare variants could be critical in interpreting some indi- vidual cases in practice [5]. However, it is hard to detect these rare variants using regression models, so we con- duct a separate analysis for the rare ... See full document
5
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
... semble methods is crucial in order to design ensemble methods well tuned to the characteristics of a specific base ...ensemble methods. To this purpose in this paper we study Support ... See full document
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A Hierarchy of Support Vector Machines for Pattern Detection
... All the training images of faces are based on the Olivetti database of 400 gray level pictures – ten frontal images for each of forty individuals. The coordinates of the eyes and the mouth of each picture were labeled ... See full document
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Extracting Important Sentences with Support Vector Machines
... Many researchers have been studied impor- tant sentence extraction since the late 1950’s (Luhn, 1958). Conventional methods focus on sentence features and define significance scores. The features include key words, ... See full document
7
Clustering Via Supervised Support Vector Machines
... The choices of the SVM regularization constant, C, has a profound distinction when used in the context of SVM-Relabeler. The choice of C in the supervised SVM controls the trade off between the training error and the ... See full document
93
Support Vector Machines for Design Space Exploration
... (DoE) methods are then often used with the goal to find the best possible model using a mini- mum number of measurements by placing them optimally inside the design ...on support vector ... See full document
6
Laplacian Support Vector Machines Trained in the Primal
... 3. An approximation of the target function with roughly the same classification accuracy as the optimal one can be achieved with a small number of iterations due to the influence of the intrinsic norm regularizer of ... See full document
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