We propose an omnivariate decision tree architecture which contains univariate, multivariate linear or nonlinear nodes, matching the complexity of the node to the complexity of the...
Abstract. This paper describes the implementation of predicate abstraction techniques to automatically compute symbolic backward reachable sets of high dimensional piecewise affine...
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
In this paper, we present DeFer--a fast, high-quality, scalable, and nonstochastic fixed-outline floorplanning algorithm. DeFer generates a nonslicing floorplan by compacting a sli...
Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from...
Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, ...