Contents
What is SOM in soft computing?
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.
What is SOM in neural network?
A self-organizing map (SOM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.
Which type of learning is exhibited by the SOM?
competitive learning
SOM is based on competitive learning. In competitive learning [7], neuron activation is a function of distance between neuron weight and input data. An activated neuron learns the most and its weights are thus modified.
What is Adaline in soft computing?
ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network uses memistors.
What are the objectives of Soft Computing?
Soft Computing provides rapid dissemination of important results in soft computing foundations, methodologies and applications. It encourages the integration of soft computing theoretical and practical results into both everyday and advanced applications.
How are the tools used in Euclidean distance analysis?
The Euclidean distance tools describe each cell’s relationship to a source or a set of sources based on the straight-line distance. There are three Euclidean tools: Euclidean Distance gives the distance from each cell in the raster to the closest source.
How is the distance of a vector defined in Euclidean space?
In more advanced areas of mathematics, when viewing Euclidean space as a vector space, its distance is associated with a norm called the Euclidean norm, defined as the distance of each vector from the origin.
Are there any limitations to using Euclidean distance?
Limitations of Euclidean distance. The Euclidean distance tools give you information according to Euclidean, or straight-line, distance. It may not be possible to travel in a straight line to a specific location; you may have to avoid obstacles such as a river or a steep slope.
Which is the only norm that is approximately Euclidean?
By Dvoretzky’s theorem, every finite-dimensional normed vector space has a high-dimensional subspace on which the norm is approximately Euclidean; the Euclidean norm is the only norm with this property. It can be extended to infinite-dimensional vector spaces as the L 2 norm or L 2 distance.