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The future versions will make an option to upload the dataset and select the features to help researchers select the best features for data classification. Where do you want to go next? Review Average 0. Your rating? You are not logged in. To discriminate your posts from the rest, you need to pick a nickname. The uniqueness of nickname is not reserved. Department of Computer Sciences, University of Texas. Ayhan Demiriz and Kristin P.

Bennett and Mark J. E-Business Department, Verizon Inc.. David Hershberger and Hillol Kargupta. Parallel Distrib. Comput, David Horn and A. The Method of Quantum Clustering. PR Siegelmann and Vladimir Vapnik. A Support Vector Method for Clustering. Intell, Edgar Acuna and Alex Rojas. Ensembles of classifiers based on Kernel density estimators. Department of Mathematics University of Puerto Rico. Manoranjan Dash and Huan Liu. Feature Selection for Clustering.

Ismail Taha and Joydeep Ghosh. Symbolic Interpretation of Artificial Neural Networks. IEEE Trans. Data Eng, Support vector domain description. Pattern Recognition Letters, Foster J. Provost and Tom Fawcett and Ron Kohavi. Stephen D. Wojciech Kwedlo and Marek Kretowski. Intell, 7. Department of Computer Science University of Massachusetts. Ke Wang and Han Chong Goh.

IJCAI 2. Ethem Alpaydin. Voting over Multiple Condensed Nearest Neighbors. Rev, Daniel C. St and Ralph W. Wilkerson and Cihan H.

Tapio Elomaa and Juho Rousu. Ron Kohavi. The Power of Decision Tables. Zoubin Ghahramani and Michael I. Learning from incomplete data. George H. John and Ron Kohavi and Karl Pfleger.

Irrelevant Features and the Subset Selection Problem. Gabor Melli. University of British Columbia. Alexander K. A hybrid method for extraction of logical rules from data. F Diercksen. James Cook University.

Michael P. Cummings and Daniel S. Myers and Marci Mangelson. Initialization of adaptive parameters in density networks. Aynur Akku and H. Altay Guvenir. Jun Wang. Classification Visualization with Shaded Similarity Matrix. Gaurav Marwah and Lois C. Igor Kononenko and Edvard Simec. Prototype based rules - a new way to understand the data. This includes ground motion, atmospheric, infrasonic, magnetotelluric, strain, hydrological, and hydroacoustic data.

IRIS facilitates seismological and geophysical research by operating and maintaining open geophysical networks and providing portable instrumentation for user-driven experiments. Instrumentation support includes engineering services, training, logistics, and best practices in equipment usage. Our mission is to advance awareness and understanding of seismology and earth science while inspiring careers in geophysics.

IRIS is a consortium of over US universities dedicated to the operation of science facilities for the acquisition, management, and distribution of seismological data, and for fostering cooperation among IRIS members, affiliates, and other organizations in order to advance seismological research and education.