high performance vector machines
High Performance Implementation of Support Vector Machines Using OpenCL
... In addition to providing a low-cost parallel system, many image processing algorithms don’t suffer from the inherent weakness of each individual core, as the tasks to be per- formed are light weight and involve small ...
101
Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity
... Support Vector Machine (SVM) (James et ...good performance from the set of data training and testing (James et ...good performance for the samples that were not observed during the ...
10
Performance Evaluation of Different Implementation Schemes of an Iterative Flow Solver on Modern Vector Machines
... a vector host (VH) and a vector engine ...a vector processor, ...The vector processor consists of eight vector cores, a 16 MB last-level cache (LLC), and six High Bandwidth ...
12
Support vector machines for texture classification
... To evaluate the effect of the polynomial degree p on the texture classification performance, a set of experiments was performed with varying degrees of p. The input window size was fixed at 17 17. Table 4 shows ...
9
Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach
... Mexico City is among the ones suffering from this problem. It is a well-known fact that individuals exposed for a long period of time to high concentration of ozone may experience serious health problems [19]. ...
10
Intelligent Optimization Methods for High Dimensional Data Classification for Support Vector Machines
... Support vector machine (SVM) is a popular pattern classification method with many application ...outstanding performance in high-dimensional data ...Support Vector Machines) and ...
11
Chunking with Support Vector Machines
... Tjong Kim Sang et al. report that they achieve higher accuracy by applying weighted voting of sys- tems which are trained using distinct chunk rep- resentations and different machine learning algo- rithms, such as MBL, ...
8
High performance speed of the induction motor drives by the predictive control using space vector modulation
... paper machines owing to their good performance provided by their solid architecture, low moment of inertia, low ripple of torque and high starting ...in high- performance ...control ...
10
Using Bag of Concepts to Improve the Performance of Support Vector Machines in Text Categorization
... sparse, high-dimensional, and ternary, which means that their dimensionality k typically is on the order of thousands and that they consist of a small number of randomly dis- tributed +1s and ...index ...
7
Online Full Text
... Support Vector Machines (PSOFuzzySVM) to predict oil well gas lift performance and production optimization in a ...The performance of a production well is a function of several ...the ...
7
Data Interpretation and Transmission Techniques for High Performance Computing Machines
... computing machines then the price of that computational machine is out of the bounds of their customer and hence product goes in loss but real time systems (like anti-missile systems, Radar systems ...
8
Robustness and Regularization of Support Vector Machines
... Second, this connection suggests novel approaches to designing good classification algorithms, in particular, designing the regularization term. In the PAC structural-risk minimization approach, regularization is chosen ...
26
Online Full Text
... support vector machine are ...support vector machine [10] [11] is one of AI techniques which serve as classifier in the ...support vector machine is to construct a hyper plane as the decision ...
5
Machine learning approach for detection of nonTor traffic
... Support Vector Machines (SVM) is a machine learning algorithm that learns to classify data using points labelled training examples falling into one or two ...
6
High Performance Control of a Switched Reluctance Machine.
... operation and high performance in all four quadrants over the entire speed range of operation. The torque ripple has been minimized to below 3% in the specified operation range. The relative acoustic noise ...
228
Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... Variable selection methods are often divided along two lines: filter and wrapper methods (Ko- havi and John, 1997). The filter approach of selecting variables serves as a preprocessing step to the induction. The main ...
15
Fast Kernel Classifiers with Online and Active Learning
... Kernel classifiers in general distinguish the few training examples named support vectors. Ker- nel classifier algorithms usually maintain an active set of potential support vectors and work by iterations. Their ...
41
Machine learning approach for detection of non-Tor Traffic
... the performance of the proposed hybrid classification algo- rithm (ANN-CFS) is compared with SVM-ANN and NB–CFS classification algorithm to classify 8 different types of traffic in the Tor ...
24
Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators
... A five-fold cross validation technique has been used to evaluate performance. Firstly we used Pg-activators as positive dataset and non Pg-activators as negative data- set. Both positive and negative datasets were ...
10
Covering Numbers and Support Vector Machines
... UPPORT vector (SV) machines [5], [6] are learning algo- rithms based on maximum margin hyperplanes [4] which make use of an implicit mapping into feature space by using a general kernel function in place of ...
12