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Articles by int numParticles

Linear Support Vector Regression Using C# with Particle Swarm Training

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By James McCaffrey, using System.IO, using System.Collections.Generic, int numParticles
| Visual Studio Magazine Verified
The Data Science Lab Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric value. A linear SVR model uses an unusual error/loss function and cannot be trained using standard techniques, and so particle swarm optimization training is used. The goal of a machine learning regression problem is to predict a single numeric value.
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Multi-Swarm Optimization for Neural Networks Using C#

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By James McCaffrey, int numParticles, namespace Multi Swarm, int numSwarms
| Visual Studio Magazine Verified
Neural Network Lab Multi-swarm optimization (MSO) is a powerful variation of particle swarm optimization. Understanding how MSO works and how to implement it can be a valuable addition to your developer toolkit. Many machine learning (ML) systems require code that minimizes error.
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Particle Swarm Optimization Using C#

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By James McCaffrey, Namespace Particle SwarmOptimization, int numParticles, int maxEpochs
| Visual Studio Magazine Verified
Neural Network Lab Particle swarm optimization isn't usually seen as the first-choice technique for training a neural network but, as James McCaffrey demonstrates, it's a useful alternative. Particle swarm optimization (PSO) is a technique for finding approximate solutions to difficult or impossible numeric optimization problems. In particular, PSO can be used to train a neural network. PSO is loosely based on the behavior of groups such as flocks of birds or schools of fish.
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