Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.

Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM) is an extremely fast and simple algorithm for generating linear and nonlinear classifiers. Kernel functions are essential in fitting GEPSVM. Usually a single kernel is used by most researchers in their studies, but the real world applications may require a combination of multiple kernel functions. There are two kind of kernels which known as global and local kernels. Global kernel functions have good generalization ability, but low learning ability. Local kernel functions have good learning ability with weak generalization. The presented approach constructs a mixed kernel function with better performance by fully combining local kernel function for strong learning ability and global kernel function for strong generalization. The Backtracking Search Algorithm (BSA) is used for determining the best value of the weight parameter between the two kernels. To evaluate the performance of the proposed approach, we applied it to public datasets from UCI repository.

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Adaptivity and Adaptability of Learning Object’s Interface Enhanced TCP Westwood Congestion Avoidance Mechanism (TCP WestwoodNew) Migration of Legacy Information System based on Business Process Theory HomeArchivesVolume 34Number 6An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Call for Paper - July 2015 Edition IJCA solicits original research papers for the July 2015 Edition. Last date of manuscript submission is June 20, 2015. Read More An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study E-mail Print inShare Download Full Text International Journal of Computer Applications © 2011 by IJCA Journal Volume 34 - Number 6 Year of Publication: 2011 Authors: M. H. Marghny Rasha M. Abd El-Aziz Ahmed I. Taloba 10.5120/4092-5420 M H Marghny, Rasha Abd M El-Aziz and Ahmed I Taloba. Article: An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study. International Journal of Computer Applications 34(6):1-6, November 2011. Full text available. BibTeX Abstract Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

ABSTRACT Homr Akarem and Homret Mikpid rare-metal granite are associated with Sn, Mo and F mineralizations. Generally, rare metal granites have special mineralogical and geochemical compositions. The studied granites are syeno- to alkali feldspar granite in composition and crystallized from F-rich melt. They were affected by weak to moderate whole rock hydrothermal alterations. The studied granites are highly fractionated (SiO2 up to 77 wt. %), and characterized by high contents of alkalis, Nb, Ta, Sn, Y, Th, Zr, Hf, Rb and F. They are post-orogenic, subalkaline and metaluminous to slightly peraluminous (ASI 0.93 to 1.07). Homr Akarem and Homret Mikpid granites are characterized by normal REEs patterns with strong negative Eu anomalies and LREE/HREE ratio is generally 1, while Homr Akarem muscovite granite shows moderate negative Eu anomalies and LREE/HREE > 1. The calculated tetrad effects of Homr Akarem and Homret Mikpid granites are low ( 0.2). This in addition to the regular distribution of high field strength elements reflects the magmatic origin. They have A2-subtype affinity which was probably derived from continental crust of igneous origin. Fluid inclusions investigation indicates the presence of three fluid generations trapped the studied granites. The early fluid generation is responsible for the pervasive alterations (feldspathization and sericitization). The second one is responsible for chloritization of biotite as well as silicification. The third generation is responsible for kaolinization process. One fluid generation was detected in Homr Akarem muscovite granite which is responsible for K-metasomatic alteration (microclinization and muscovitization).

ABSTRACT Homr Akarem and Homret Mikpid rare-metal granite are associated with Sn, Mo and F mineralizations. Generally, rare metal granites have special mineralogical and geochemical compositions. The studied granites are syeno- to alkali feldspar granite in composition and crystallized from F-rich melt. They were affected by weak to moderate whole rock hydrothermal alterations. The studied granites are highly fractionated (SiO2 up to 77 wt. %), and characterized by high contents of alkalis, Nb, Ta, Sn, Y, Th, Zr, Hf, Rb and F. They are post-orogenic, subalkaline and metaluminous to slightly peraluminous (ASI 0.93 to 1.07). Homr Akarem and Homret Mikpid granites are characterized by normal REEs patterns with strong negative Eu anomalies and LREE/HREE ratio is generally 1, while Homr Akarem muscovite granite shows moderate negative Eu anomalies and LREE/HREE > 1. The calculated tetrad effects of Homr Akarem and Homret Mikpid granites are low ( 0.2). This in addition to the regular distribution of high field strength elements reflects the magmatic origin. They have A2-subtype affinity which was probably derived from continental crust of igneous origin. Fluid inclusions investigation indicates the presence of three fluid generations trapped the studied granites. The early fluid generation is responsible for the pervasive alterations (feldspathization and sericitization). The second one is responsible for chloritization of biotite as well as silicification. The third generation is responsible for kaolinization process. One fluid generation was detected in Homr Akarem muscovite granite which is responsible for K-metasomatic alteration (microclinization and muscovitization).

ABSTRACT Homr Akarem and Homret Mikpid rare-metal granite are associated with Sn, Mo and F mineralizations. Generally, rare metal granites have special mineralogical and geochemical compositions. The studied granites are syeno- to alkali feldspar granite in composition and crystallized from F-rich melt. They were affected by weak to moderate whole rock hydrothermal alterations. The studied granites are highly fractionated (SiO2 up to 77 wt. %), and characterized by high contents of alkalis, Nb, Ta, Sn, Y, Th, Zr, Hf, Rb and F. They are post-orogenic, subalkaline and metaluminous to slightly peraluminous (ASI 0.93 to 1.07). Homr Akarem and Homret Mikpid granites are characterized by normal REEs patterns with strong negative Eu anomalies and LREE/HREE ratio is generally 1, while Homr Akarem muscovite granite shows moderate negative Eu anomalies and LREE/HREE > 1. The calculated tetrad effects of Homr Akarem and Homret Mikpid granites are low ( 0.2). This in addition to the regular distribution of high field strength elements reflects the magmatic origin. They have A2-subtype affinity which was probably derived from continental crust of igneous origin. Fluid inclusions investigation indicates the presence of three fluid generations trapped the studied granites. The early fluid generation is responsible for the pervasive alterations (feldspathization and sericitization). The second one is responsible for chloritization of biotite as well as silicification. The third generation is responsible for kaolinization process. One fluid generation was detected in Homr Akarem muscovite granite which is responsible for K-metasomatic alteration (microclinization and muscovitization).

ABSTRACT Homr Akarem and Homret Mikpid rare-metal granite are associated with Sn, Mo and F mineralizations. Generally, rare metal granites have special mineralogical and geochemical compositions. The studied granites are syeno- to alkali feldspar granite in composition and crystallized from F-rich melt. They were affected by weak to moderate whole rock hydrothermal alterations. The studied granites are highly fractionated (SiO2 up to 77 wt. %), and characterized by high contents of alkalis, Nb, Ta, Sn, Y, Th, Zr, Hf, Rb and F. They are post-orogenic, subalkaline and metaluminous to slightly peraluminous (ASI 0.93 to 1.07). Homr Akarem and Homret Mikpid granites are characterized by normal REEs patterns with strong negative Eu anomalies and LREE/HREE ratio is generally 1, while Homr Akarem muscovite granite shows moderate negative Eu anomalies and LREE/HREE > 1. The calculated tetrad effects of Homr Akarem and Homret Mikpid granites are low ( 0.2). This in addition to the regular distribution of high field strength elements reflects the magmatic origin. They have A2-subtype affinity which was probably derived from continental crust of igneous origin. Fluid inclusions investigation indicates the presence of three fluid generations trapped the studied granites. The early fluid generation is responsible for the pervasive alterations (feldspathization and sericitization). The second one is responsible for chloritization of biotite as well as silicification. The third generation is responsible for kaolinization process. One fluid generation was detected in Homr Akarem muscovite granite which is responsible for K-metasomatic alteration (microclinization and muscovitization).