What are the parameters of PSO?

The basic PSO is influenced by a number of control parameters, namely the dimension of the problem, number of particles, acceleration coefficients, inertia weight, neighbor- hood size, number of iterations, and the random values that scale the contribution of the cognitive and social components.

What is parameter Optimisation?

A fancy name for training: the selection of parameter values, which are optimal in some desired sense (eg. minimize an objective function you choose over a dataset you choose). The parameters are the weights and biases of the network.

What is PSO method?

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The algorithm was simplified and it was observed to be performing optimization.

What is optimization in SVM?

As already discussed, SVM aims at maximizing the geometric margin and returns the corresponding hyperplane. Such points are called as support vectors (fig. – 1). Therefore, the optimization problem as defined above is equivalent to the problem of maximizing the margin value (not geometric/functional margin values).

What is PSO swarm size?

The fourth control parameter in classical PSO is the swarm size (also called population size, or the number of particles). The swarm size may be considered the most “basic” control parameter of PSO, as it simply defines the number of individuals in the swarm, and hence its setting can hardly be avoided.

What is PSO inertia weight?

The Inertia Weight determines the contribution rate of a particle’s previous velocity to its velocity at the current time step. The basic PSO, presented by Eberhart and Kennedy in 1995 [1], has no Inertia Weight.

What is the reason for parameter optimization?

Optimized parameter values will enable the model to perform the task with relative accuracy. The cost function inputs a set of parameters and outputs a cost, measuring how well that set of parameters performs the task (on the training set).

What is parameter optimization in machine learning?

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.

How do you use PSO feature selection?

To apply PSO to the feature selection problem you need first to map features selection/deselection using a representation suitable for PSO (usually continuous values representing particle’s position), develop the particles evaluation function, generate the initial swarm, and repeatedly apply the PSO steps of particles …

What are the roles of Optimizer kernel function in SVM algorithm?

The kernel functions are used as parameters in the SVM codes. They help to determine the shape of the hyperplane and decision boundary. We can set the value of the kernel parameter in the SVM code. The value can be any type of kernel from linear to polynomial.

Why SVM is called maximum margin classifier?

Support vector machines attempt to pass a linearly separable hyperplane through a dataset in order to classify the data into two groups. This is the Maximum Margin Classifier. It maximizes the margin of the hyperplane. This is the best hyperplane because it reduces the generalization error the most.