Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution. If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see
Specifically, we present a new approach to horizon line detection by coupling machine learning with dynamic programming. Given an image, the Canny edge detector is applied first and keeping only those edges which survive over a wide range of thresholds. We refer to the surviving edges as ...
1991. Learning qualitative models of dynamic systems. In Proceedings of the International Workshop on Inductive Logic Programming, pages 207-224. Coiera, E. 1989. Generating qualitative models from example behaviours. DCS Report 8901, Department of Computer Science, University of New South Wales, ...
Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the ...
This book is about dynamic programming and its applications in economics, finance, and adjacent fields. It brings together recent innovations in the theory of dynamic programming and provides applications and code. - free book at FreeComputerBooks.com
To predict the surface chloride concentrations in concrete containing waste material, Ahmad, et al. [27] used gene expression programming (GEP), decision trees (DT), and ANN. Additionally, Ahmad, et al. [28] presented the applications of DT, ANN and gradient boosting (GB) to predict the ...
in the face of approximation techniques such as Q-learning and TD-learning [26], [6], [10]. These techniques are designed to approximate a solution to a dynamic programming equation within a prescribed finite-dimensional function class. A key determinant of the success of these techniques is...
Then, the dynamic programming model is incorporated into the optimal order scheduling strategy. Python is employed to simulate the order scheduling in manufacturing enterprises. Based on survey data, the superiority of the proposed model compared to traditional first come, first served order scheduling ...
Starting with ML and finishing with mathematical programming will permit to solve globally larger problems and will avoid the loss of optimality due to heuristic decomposition in small time slices in the rolling horizon approach. In addition, the use of a rolling-horizon approach was improved to ...
Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patte