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Self-organizing map

Self-organizing map
A self-organizing map (SOM) or self-organizing feature map (SOFM) 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. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map or network.[1][2] Like most artificial neural networks, SOMs operate in two modes: training and mapping. A self-organizing map consists of components called nodes or neurons. Large SOMs display emergent properties. Learning algorithm[edit] Variables[edit]

http://en.wikipedia.org/wiki/Self-organizing_map

Related:  Machine Learning

Multilayer perceptron A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs. A MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training the network.[1][2] MLP is a modification of the standard linear perceptron and can distinguish data that are not linearly separable.[3] Theory[edit] Activation function[edit]

Teuvo Kohonen Teuvo Kohonen (born July 11, 1934) is a prominent Finnish academician (Dr. Eng.) and researcher. He is currently professor emeritus of the Academy of Finland. Prof. Kohonen has made many contributions to the field of artificial neural networks, including the Learning Vector Quantization algorithm, fundamental theories of distributed associative memory and optimal associative mappings, the learning subspace method and novel algorithms for symbol processing like redundant hash addressing. He has published several books and over 300 peer-reviewed papers.

Growing self-organizing map A growing self-organizing map (GSOM) is a growing variant of the popular self-organizing map (SOM). The GSOM was developed to address the issue of identifying a suitable map size in the SOM. It starts with a minimal number of nodes (usually 4) and grows new nodes on the boundary based on a heuristic. By using the value called Spread Factor (SF), the data analyst has the ability to control the growth of the GSOM. All the starting nodes of the GSOM are boundary nodes, i.e. each node has the freedom to grow in its own direction at the beginning. (Fig. 1) New Nodes are grown from the boundary nodes. Shocking, Football, Tornado, Porn: Science Explains Why You’ll Read This Article Don’t write about finance. Or markets. Or, for the sake of little children everywhere, petroleum. These public affairs are highly unappealing topics to most of us. Stick to safer bets like religion, sports, crime, or--and this is one of those rare gems in publishing that you can never predict--disasters! What you’ve just read isn’t just my editorial advice, it’s one of the chief findings of a recent study by the University of Bristol’s Intelligent Systems Laboratory, which parsed over 2 million “Top Stories” or “Most Popular” articles by news outlets including NPR, the New York Times, the BBC, Forbes, and Reuters to discover the trends behind the world’s most-read stories.

Connectionism Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience, and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units. There are many forms of connectionism, but the most common forms use neural network models. Basic principles[edit]

Protein Secondary Structure Prediction with Neural Nets: Feed-Forward Networks Introduction to feed-forward nets Feed-forward nets are the most well-known and widely-used class of neural network. The popularity of feed-forward networks derives from the fact that they have been applied successfully to a wide range of information processing tasks in such diverse fields as speech recognition, financial prediction, image compression, medical diagnosis and protein structure prediction; new applications are being discovered all the time. (For a useful survey of practical applications for feed-forward networks, see [Lisboa, 1992].) In common with all neural networks, feed-forward networks are trained, rather than programmed, to carry out the chosen information processing tasks. Training a feed-forward net involves adjusting the network so that it is able to produce a specific output for each of a given set of input patterns.

Scholarpedia Figure 1: The array of nodes in a two-dimensional SOM grid. The Self-Organizing Map (SOM), commonly also known as Kohonen network (Kohonen 1982, Kohonen 2001) is a computational method for the visualization and analysis of high-dimensional data, especially experimentally acquired information. Introduction The Self-Organizing Map defines an ordered mapping, a kind of projection from a set of given data items onto a regular, usually two-dimensional grid. Singular Value Decomposition of a Matrix Description Compute the singular-value decomposition of a rectangular matrix. Usage svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE) La.svd(x, nu = min(n, p), nv = min(n, p)) Arguments Details

Microsoft explains Xbox One’s new griefer-separating reputation system On the Xbox 360, your Xbox Live reputation is a simple five-star rating that is often ignored by the community at large. On the Xbox One, though, the reputation system will get a complete overhaul that will use more detailed monitoring and reporting tools to separate antisocial players from the rest of the community. Xbox Community Manager Larry "Major Nelson" Hryb first mentioned the reputation system overhaul during last month's E3. He discussed how players would be grouped into broad categories of "Good Player," "Need Improvement," or "Avoid Me" based on feedback from fellow players and from automated logging of things like "block" or "mute" actions. In a new interview with the UK's Official Xbox Magazine, Microsoft Senior Product Manager Mike Lavin discussed the system in a little more detail. Players who prefer to stick with a party of known people in their friends lists won't be affected by the reputation system changes, Lavin said.

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