Abstract not found

learning algorithms effectively. Author Keywords Feature labeling, locally weighted logistic regression, machine

Oftentimes, the problem faced by researchers applying reinforcement learning to a nontrivial robotics problem is that they run head-on into the curse of dimensionality. This is a particular problem for those researchers using discrete-state algorithms, as the number of states exponentially increase with the complexity of the problem. This thesis provides a method by which the performance of a discrete-state algorithm can be improved when applied to a continuous-state problem in combination with a function approximator. The method consists of two steps. The first step consists of learning the value function over a small number of discrete states. The second step involves using the function approximator to generalize from those discrete states to a continuous state space.

This paper describes a two-part method of dealing with the large or continuous state spaces encountered in large reinforcement learning (RL) problems. The problem is first approached by learning the value function over a coarse quantization of states. The value function is then approximated to extend the results to the original large or continuous state space. This second step improves the problem performance by allowing the agent to use all of the originally available state information. Performance of this method is significantly improved compared to the original performance of the RL algorithm. The nature of this method makes it directly applicable to

probability values. However, in some applications a continuous result is expected. In this paper, a scheme based on DS reasoning and locally weighted regression is proposed to fuse the data obtained from aircraft corrosion damage inspections. The proposed approach implements a pairwise regression

, however, the actual thermal comfort could be significantly different, and so energy may be wasted trying to achieve unwanted conditions. In this paper, we apply Locally Weighted Regression with Adaptive Bandwidth (LRAB) to learn individual occupant preferences based on historical reports. As an initial

time series. The multivariate locally weighted polynomial fit is studied for this purpose. In the nonparametric regression context, explicit asymptotic expansions for the conditional bias and conditional covariance matrix of the regression and partial derivative estimators are derived for both

in observed state variables are due to faults or external driving conditions. In this paper, we present a comparison between locally weighted regression (a local non-linear model) and polynomial regression (a global non-linear model) in the context of fault detection and diagnosis of överchargedänd

Abstract. We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained with locally weighted regression

, i.e., traces provided by the user; to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our