Xingye Qiao

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Classification is an important topic in statistics and machine learning with great potential in many real applications. In this paper, we investigate two popular large-margin classification methods, Support Vector Machine (SVM) and Distance Weighted Discrimination (DWD), under two contexts: the high-dimensional, low-sample size data and the imbalanced data.(More)
In multicategory classification, standard techniques typically treat all classes equally. This treatment can be problematic when the dataset is unbalanced in the sense that certain classes have very small class proportions compared to others. The minority classes may be ignored or discounted during the classification process due to their small proportions.(More)
BACKGROUND The primary objectives of this paper are: 1.) to apply Statistical Learning Theory (SLT), specifically Partial Least Squares (PLS) and Kernelized PLS (K-PLS), to the universal "feature-rich/case-poor" (also known as "large p small n", or "high-dimension, low-sample size") microarray problem by eliminating those features (or probes) that do not(More)
Fitted Q-Iteration (FQI) is a popular approximate value iteration (AVI) approach that makes effective use of off-policy data. FQI uses a 1-step return value update which does not exploit the sequential nature of trajectory data. Complex returns (weighted averages of the n-step returns) use tra-jectory data more effectively, but have not been used in an AVI(More)
Classification and clustering are both important topics in statistical learning. A natural question herein is whether predefined classes are really different from one another , or whether clusters are really there. Specifically, we may be interested in knowing whether the two classes defined by some class labels (when they are provided), or the two clusters(More)
Approximate value iteration (AVI) is a widely used technique in reinforcement learning. Most AVI methods do not take full advantage of the sequential relationship between samples within a trajectory in deriving value estimates, due to the challenges in dealing with the inherent bias and variance in the n-step returns. We propose a bounding method which uses(More)
Outline Motivations Classification instability and its minimax properties Stabilized nearest neighbor classifier Experiments Sun, Wei (Purdue) Nearest Neighbor Classifier with Optimal Stability Motivation Begley and Ellis (Nature, 2012) found that 47/53 medical research papers on the subject of cancer were irreproducible. Motivation In the paper " Stability(More)