Christopher J. Gatti

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The purpose of this paper is to evaluate and benchmark ensemble methods for time series prediction for daily currency exchange rates using ensemble feedforward neural networks and kernel partial least squares (K-PLS). Best-practice forecasting methods for the US Dollar (USD) per Indian Rupee (IR) are applied for training, validating, and testing the machine(More)
Pathology of the superior glenoid labrum is a common source of musculoskeletal pain and disability. One of the proposed mechanisms of injury to the labrum is superior humeral head migration, which can be seen with rotator cuff insufficiency. Due to the size, anatomical location, and complex composition of the labrum, laboratory experiments have many(More)
This study uses a design of experiments approach to understand the behavior of a neural network to learn the mountain car domain using the TD(λ) algorithm. A large experiment is first performed to characterize the probability of empirical convergence based on three parameters of the TD(λ) algorithm (λ, γ,), and a logistic regression model is fitted to this(More)
This work presents a simple implementation of reinforcement learning, using the temporal difference algorithm and a neural network, applied to the board game of Chung Toi, which is a challenging variation of Tic-Tac-Toe. The implementation of this learning algorithm is fully described and includes all parameter settings and various techniques to improve the(More)
This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s. Our study is based on 80,757 published journal abstracts from 37 of the leading OR/MS journals. We have developed a topic model, using Latent Dirichlet Allocation (LDA), and extend this(More)
We use a reinforcement learning approach to learn a real world control problem, the truck backer-upper problem. In this problem, a tractor trailer truck must be backed into a loading dock from an arbitrary location and orientation. Our approach uses the temporal difference algorithm using a neural network as the value function approximator. The novelty of(More)