A Novel Approach to Distributed Multi-Class SVM

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research proposes a novel algorithm that implements the Support Vector Machine over a multi-class dataset and is efficient in a distributed environment (here, Hadoop). The idea is to divide the dataset into half recursively and thus compute the optimal Support Vector Machine for this half during the training phase, much like a divide and conquer approach. While testing, this structure has been effectively exploited to significantly reduce the prediction time. Our algorithm has shown better computation time during the prediction phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the dataset grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.Comment: 8 Page

Distributed Multi Class SVM for Large Data Sets

Data mining algorithms are originally designed by assuming the data is available at one centralized site.These algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability.Comment: Presente in the WCI, Kochi, India, 201

Transport study of Berry's phase, the resistivity rule, and quantum Hall effect in graphite

Transport measurements indicate strong oscillations in the Hall-,$R_{xy}$, and the diagonal-, $R_{xx}$, resistances and exhibit Hall plateaus at the lowest temperatures, in three-dimensional Highly Oriented Pyrolytic Graphite (HOPG). At the same time, a comparative Shubnikov-de Haas-oscillations-based Berry's phase analysis indicates that graphite is unlike the GaAs/AlGaAs 2D electron system, the 3D n-GaAs epilayer, semiconducting $Hg_{0.8}Cd_{0.2}Te$, and some other systems. Finally, we observe the transport data to follow $B\times dR_{xy}/dB \approx - \Delta R_{xx}$. This feature is consistent with the observed relative phases of the oscillatory $R_{xx}$ and $R_{xy}$.Comment: 5 pages, 4 figure

Student Perception Towards Social Networking Site in Theni District

Social Networking Sites are getting more popular and it has become a vital part of our social life. New digital media have dramatically altered the communication landscape, especially for youth. Internet is a very powerful platform that has changed the way people do things. Social Networking Site is a wonderful innovation in the Internet age whereby people are interconnected in the global network society. Social media has a great effect on people\u27s lives and millions of students are spending many hours on social networking sites. As social media sites continue to grow in popularity, it is our premise that technology is a vital part of today\u27s student success equation. This study aims to investigate student\u27s perception towards Social Networking Sits in Theni District

Force user's manual: A portable, parallel FORTRAN

The use of Force, a parallel, portable FORTRAN on shared memory parallel computers is described. Force simplifies writing code for parallel computers and, once the parallel code is written, it is easily ported to computers on which Force is installed. Although Force is nearly the same for all computers, specific details are included for the Cray-2, Cray-YMP, Convex 220, Flex/32, Encore, Sequent, Alliant computers on which it is installed

The FORCE: A portable parallel programming language supporting computational structural mechanics

This project supports the conversion of codes in Computational Structural Mechanics (CSM) to a parallel form which will efficiently exploit the computational power available from multiprocessors. The work is a part of a comprehensive, FORTRAN-based system to form a basis for a parallel version of the NICE/SPAR combination which will form the CSM Testbed. The software is macro-based and rests on the force methodology developed by the principal investigator in connection with an early scientific multiprocessor. Machine independence is an important characteristic of the system so that retargeting it to the Flex/32, or any other multiprocessor on which NICE/SPAR might be imnplemented, is well supported. The principal investigator has experience in producing parallel software for both full and sparse systems of linear equations using the force macros. Other researchers have used the Force in finite element programs. It has been possible to rapidly develop software which performs at maximum efficiency on a multiprocessor. The inherent machine independence of the system also means that the parallelization will not be limited to a specific multiprocessor