Wed Sep 28, 12:00, WeH 1327 Topic: Kd-trees for efficient regression Kan Deng* Kernel Regression is a well-known memory-based technique for function approximation. It works by storing all {input, output} training patterns in memory. To predict an output for a new input pattern, it computes a weighted average of some or all of the outputs in the memory, where the weights are a function of the distances from the inputs in memory to the query. The drawback of Kernel Regression is that enumerating these weights can be expensive when the size of the memory is large. In this talk, we demonstrate that the efficiency of Kernel Regression can be greatly improved by the use of kd-trees. There have previously been several attempts to use kd-trees to speed up Kernel Regression and nearest neighbor methods. Our algorithm is new and I will explain its advantages in the talk. In addition to regression, the framework of kd-trees holds potential for many other applications. We will discuss these at the end of the talk. * joint work with Andrew Moore