In this article, we talk about Particle Swarm Optimization. In last post, we talked Computational tools, including Cellular automata Ant Colony Optimization Particle Swarm Optimization Particle Swarm Optimization Originally intended to simulate bird flocks and…

Intelligence <- Social behavior Intelligence can emerge from social interaction. Emergent behaviour – when a group behaves in ways that were not ”programmed” into its members Swarm intelligence simulated social interaction emergent collective intelligence i…

EC is parallel search (many balls in the landscape). Want to prevent all individuals from converging to the same solution (premature convergence – a common problem) The task of mutation is to prevent this – to maintain diversity in the population. To inject ne…

From course, I learned Evolutional Computing. Evolutionary computing (EC) population method Used for problems where the task is to maximize some measure of success Same family of problems as in RL, but very different methods Methods inspired by genetics, natur…

Let us firstly revisit completive learning and SOFM A node position (in the input space) is its weight vector, i.e. nodes move around in the same space as the data. Purpose of unsupervised learning is (usually) to cluster for classification, or for modelling d…

In this note, we are going talk about Deep Learning very briefly. RBFNs are feedforward networks, where: The hidden nodes (the RBF nodes) compute distances, instead of weighted sums. The hidden layer activation functions are Gaussians (or similar), instead of …

To learn from unlabeled data (only inputs, no target information). Most often for classification. Requires that class membership can be detected by structural properties (features) in the data, and that the learning system can be fund these features. Following…

From today, we are going to introduce reinforcement learning(RL). Forget ANNs for a while - RL is a field in its own right, independent from ANNs. However, as we will see, ANNs are sometimes used in RL. Definition: Learning by interaction with an environment, …

In this course, we learn more on extensions. and prior knowledge. We want to minimize the number of hidden nodes(=> less overfitting) The approaches: start with a large networks and prune. start with a small networks and grow. There are two example. Let eac…

Let's continue talk about overtraining. The number of training set samples should be much larger than the number of weights roughly for ​ \(N^2\)(N = the number of weights). minimize the number of nodes and layers(weights) noise injection and noise to the…