Exploratory analysis of speedup learning data using expectation maximization

Exploratory Analysis of Speedup Learning Data using Expectation Maximization

Department of School of Department of Computer Science Management Sciences Computer Science and Engineering University of Iowa Carnegie-Mellon University University of California at San Diego Iowa City, IA 52242 Pittsburgh, PA 15213 La Jolla, CA 92093 Abstract Experimental evaluations of speedup learning methods have in the past used nonparametric hypothesis testing to determine whether or not ...

Exploratory Analysis of Speedup Learning Data Using Epectation Maximization

Using Expectation-Maximization for Reinforcement Learning

We discuss Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-...

Aerial Lidar Data Classification using Expectation-Maximization

We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged)...

Data Synthesis with Expectation-Maximization

A problem of increasing importance in computer graphics is to generate data with the style of some previous training data, but satisfying new constraints. If we use a probabilistic latent variable model, then learning the model will normally be performed using Expectation-Maximization (EM), or one of its generalizations. We show that data synthesis for such problems can also be performed using ...