Posts Tagged ‘ PROC DISCRIM ’

Kaggle Digit Recoginizer: SAS k-Nearest Neighbor solution

December 11, 2012
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Kaggle Digit Recoginizer: SAS k-Nearest Neighbor solution

Kaggle is hosting an educational data mining competition: Kaggle Digit Recognizer, using MNIST data. Handwritten digit recognition is one of the few applications that kNN classifier performs well. Of course, the benchmark kNN classifier provided ...
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KNN Classification and Regression in SAS

November 25, 2012
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kNN stands for k Nearest Neighbor. In data mining and predictive modeling, it refers to a memory-based (or instance-based) algorithm for classification and regression problems. It is a widely used algorithm with many successfully applications in medi...
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Regularized Discriminant Analysis

April 10, 2011
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Demo SAS implementation of Regularized (Linear) Discriminate Analysis of J. Friedman (1989). Simpler introduction can be found at . Regularized QDA follows similarly. To save coding, I called R within SAS to finish the computation. For details...
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Using SAS to find the best k for k-Nearest Neighbor classification

April 7, 2011
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Using SAS to find the best k for k-Nearest Neighbor classification

Least-square (regression) and nearest-neighbor are the most fundamental methodologies for supervised classification . Even though they are pretty old, they are still popular and widely used in academia and industry. There is a trade-off in comp...
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An Economic Approach for a Class of Dimensionality Reduction Techniques

July 30, 2010
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Just back from KDD2010. In the conference, there are several papers that interested me. On the computation side, Liang Sun et al.'s paper , "A Scalable Two-Stage Approach for a Class of Dimensionality Reduction Techniques" caught my eyes. Liang p...
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K-Nearest Neighbor in SAS

May 5, 2010
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K-Nearest-Neighbor, aka KNN, is a widely used data mining tool and is often called memory-based/case-based/instance-based method as no model is fit. A good introduction to KNN can be find at , or @ Wiki. Typically, KNN algorithm relies on a soph...
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