Ruben Van Havermaet's Library tagged learning → View Popular

Connectivism is the integration of principles explored by chaos, network,
and complexity and self-organization theories. Learning is a process that
occurs within nebulous environments of shifting core elements –
not entirely under the control of the individual. Learning (defined as
actionable knowledge) can reside outside of ourselves (within an organization
or a database), is focused on connecting specialized information sets,
and the connections that enable us to learn more are more important than
our current state of knowing

22 Mar 08

Self-organizing map - Wikipedia, the free encyclopedia

en.wikipedia.org/...Kohonen_maps - Preview

ai computer science learning

unsupervised learning
useful for visualizing low-dimensional views of high-dimensional data
21 more annotations...
- operate in two modes: training and mapping
- consists of components called nodes or neurons
- Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space
- describes a mapping from a higher dimensional input space to a lower dimensional map space
- small number of nodes behave in a way that is similar to K-means
- U-matrix value of a particular node is the average distance between the node and its closest neighbors
- Large SOM display properties which are emergent
- goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns
- weights
- sampled evenly from the subspace spanned by the two largest principal component eigenvectors
- much faster because the initial weights already give good approximation of SOM weights
- Euclidean distance to all weight vectors is computed
- training example is fed to the network, its
- weights of the BMU and neurons close to it in the SOM lattice are adjusted towards the input vector
- best matching unit
- Wv(t + 1) = Wv(t) + Θ (v, t) α(t)(D(t) - Wv(t)),
- monotonically decreasing learning coefficient
- At the beginning when the neighborhood is broad, the self-organizing takes place on the global scale. When the neighborhood has shrunk to just a couple of neurons the weights are converging to local estimates
- any kind of object which can be represented digitally and which has an appropriate distance measure associated with it and in which the necessary operations for training are possible can be used to construct a self-organizing map
- Randomize the map's nodes' weight vectors
  Grab an input vector
  Traverse each node in the map
  
  Use Euclidean distance formula to find similarity between the input vector and the map's node's weight vector
  Track the node that produces the smallest distance (this node is the best matching unit, BMU)
  Update the nodes in the neighbourhood of BMU by pulling them closer to the input vector
  
  Wv(t + 1) = Wv(t) + Θ(t)α(t)(D(t) - Wv(t))
  Increment t and repeat while t < λ
- SOM forms a semantic map where similar samples are mapped close together and dissimilar apart

08 Feb 08

Neural Networks - A Systematic Introduction

a free neural network book available online

page.mi.fu-berlin.de/...index.html - Preview

AI computerscience ebooks learning neuralnetworks research

18 Jan 08

The Anti-Mac: Violating the Macintosh Human Interface Guidelines

www.sigchi.org/...gen_bdy.htm - Preview

computerscience culture learning media newmedia ui

17 Jan 08

A tutorial describing how to paint with smoke

www.wikihow.com/Paint-With-Smoke - Preview

art learning painting research tutorials

05 Nov 07

KASK.be: opleiding nieuwe media

een opleiding nieuwe media in gent

www.kask.be/index.php - Preview

art artscience learning newmedia research

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