ct ivat ion Function for inimieat ion Abstract
... NP-hard problems. Nevertheless, finding a global minimum for the energy function is not guaranteed, and even a local minimum may take an exponential number of steps. We propose an improvement to the standard activation function used for such networks. The improved algorithm guarantees that a global ...
... NP-hard problems. Nevertheless, finding a global minimum for the energy function is not guaranteed, and even a local minimum may take an exponential number of steps. We propose an improvement to the standard activation function used for such networks. The improved algorithm guarantees that a global ...
Classification with an improved Decision Tree Algorithm
... C4.5 allows the attributes with different costs. Post Pruning - C4.5 creates first decision tree and after creation, it goes back through the tree and attempts to remove branches that do not help by replacing them with leaf nodes. ...
... C4.5 allows the attributes with different costs. Post Pruning - C4.5 creates first decision tree and after creation, it goes back through the tree and attempts to remove branches that do not help by replacing them with leaf nodes. ...
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... Course Objective: The objective of this course is to make the students Familiar with the efficient parallel algorithms related to many areas of computer science: expression computation, sorting, graph-theoretic problems, etc. Familiar with the basic issues of implementing parallel algorithms. ...
... Course Objective: The objective of this course is to make the students Familiar with the efficient parallel algorithms related to many areas of computer science: expression computation, sorting, graph-theoretic problems, etc. Familiar with the basic issues of implementing parallel algorithms. ...
Hybrid Evolutionary Learning Approaches for The Virus Game
... The fitness of each individual in the population of parameter sets is measured as the reciprocal of the average RMSE of this parameter set over all neural networks of the current neural network population when trained using RPROP. The fitness of a neural network topology is measured as the reciproca ...
... The fitness of each individual in the population of parameter sets is measured as the reciprocal of the average RMSE of this parameter set over all neural networks of the current neural network population when trained using RPROP. The fitness of a neural network topology is measured as the reciproca ...
Model Predictive Control: History and Development Ruchika, Neha Raghu
... generation of MPC technology. Later on a second generation of MPC such as quadratic dynamic matrix control (QDMC) came into picture. Cutler et. al. first described the QDMC algorithm in a 1983 AlChE conference paper [9]. Garcia and Morshedi presented a more comprehensive description some years later ...
... generation of MPC technology. Later on a second generation of MPC such as quadratic dynamic matrix control (QDMC) came into picture. Cutler et. al. first described the QDMC algorithm in a 1983 AlChE conference paper [9]. Garcia and Morshedi presented a more comprehensive description some years later ...
The common ancestor process revisited
... from the very beginning. The results only have partial interpretations in terms of the graphical representation of the model (i.e., the representation that makes individual lineages and their interactions explicit). The aim of this article is to complement these approaches by starting from the graph ...
... from the very beginning. The results only have partial interpretations in terms of the graphical representation of the model (i.e., the representation that makes individual lineages and their interactions explicit). The aim of this article is to complement these approaches by starting from the graph ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.