Extended Kohonen Maps Home A Kohonen map is a Self-Organizing Map (SOM) used to order a set of high-dimensional vectors. It can be used to clarify relations in a complex set of data, by revealing some inherent order. This webpage gives access to software that can be used to create standard Kohonen maps, as well as some extensions. Update 2001/11/16: Recompiled koh.exe for Windows allows for processing of much larger data files. Contents: Note: Images on this webpage use grey-scales to convey information. Literature The primary source on Kohonen maps is: Teuvo Kohonen.Self-Organization and Associative Memory. The extensions were first described in: Peter Kleiweg.Neurale netwerken: Een inleidende cursus met practica voor de studie Alfa-Informatica. Kohonen's algorithm A Kohonen map is created using Artificial Neural Network techniques. The result of the training is that a pattern of organization emerges in the map. To demonstrate this algorithm, Kohonen used the set of 32 vectors reproduced in the table below. koh.c
Data Mining Algorithms In R/Clustering/Self-Organizing Maps (SOM) 1: Initialize the centroids. 2: repeat 3: Select the next object. 4: Determine the closest centroid to the object. 5: Update this centroid and the centroids that are close, i.e., in a specified neighborhood. 6: until The centroids don't change much or a threshold is exceeded. 7: Assign each object to its closest centroid and return the centroids and clusters. The kohonen package implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.The som package provides functions for self-organizing maps.The wccsom package SOM networks for comparing patterns with peak shifts. som(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01), radius = quantile(nhbrdist, 0.67) * c(1, -1), init, toroidal = FALSE, n.hood, keep.data = TRUE) the arguments are: return an object of class "kohonen" with components: data: a matrix, with each row representing an object.Y: property that is to be modelled.
Home Page of Thorsten Joachims · International Conference on Machine Learning (ICML), Program Chair (with Johannes Fuernkranz), 2010. · Journal of Machine Learning Research (JMLR) (action editor, 2004 - 2009). · Machine Learning Journal (MLJ) (action editor). · Journal of Artificial Intelligence Research (JAIR) (advisory board member). · Data Mining and Knowledge Discovery Journal (DMKD) (action editor, 2005 - 2008). · Special Issue on Learning to Rank for IR, Information Retrieval Journal, Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, T. · Special Issue on Automated Text Categorization, Journal on Intelligent Information Systems, T. · Special Issue on Text-Mining, Zeitschrift Künstliche Intelligenz, Vol. 2, 2002. · Enriching Information Retrieval, P. · Redundancy, Diversity, and Interdependent Document Relevance (IDR), P. · Beyond Binary Relevance, P. · Machine Learning for Web Search, D. · Learning to Rank for Information Retrieval, T. · Learning in Structured Output Spaces, U. · Learning for Text Categorization.
Self Organizing Map AI for Pictures Introduction this article is about creating an app to cluster and search for related pictures. i got the basic idea from a Longhorn demo in which they showed similar functionality. in the demo, they selected an image of the sunset, and the program was able to search the other images on the hard drive and return similar images. there are other photo library applications that offer similar functionality. honestly ... i thought that was pretty cool, and wanted to have some idea how they might be doing that. internally, i do not know how they actually operate ... but this article will show one possibility. also writing this article to continue my AI training Kohonen SOM luckily there is a type of NN that works with unsupervised training. i'm guessing that it is the 2nd or 3rd most popular type of NN? anyways, that is my current understanding; here are some other articles i recommend 1) Grid Layout 2) Color Grouping 3) Blog Community (OUCH!) 4) Picture Similarity
Selbstorganisierende Karte Als Selbstorganisierende Karten, Kohonenkarten oder Kohonennetze (nach Teuvo Kohonen; englisch self-organizing map, SOM bzw. self-organizing feature map, SOFM) bezeichnet man eine Art von künstlichen neuronalen Netzen. Sie sind als unüberwachtes Lernverfahren ein leistungsfähiges Werkzeug des Data-Mining. Ihr Funktionsprinzip beruht auf der biologischen Erkenntnis, dass viele Strukturen im Gehirn eine lineare oder planare Topologie aufweisen. Die Signale des Eingangsraums, z. B. visuelle Reize, sind jedoch multidimensional. Es stellt sich also die Frage, wie diese multidimensionalen Eindrücke durch planare Strukturen verarbeitet werden. Wird nun ein Signal an diese Karte herangeführt, so werden nur diejenigen Gebiete der Karte erregt, die dem Signal ähnlich sind. Anwendung finden selbstorganisierende Karten zum Beispiel in der Computergrafik als Quantisierungsalgorithmus zur Farbreduktion von Rastergrafikdaten und in der Bioinformatik zur Clusteranalyse. Struktur und Lernen[Bearbeiten]
Kohonen Networks Kohonen Networks Introduction In this tutorial you will learn about: Unsupervised Learning Kohonen Networks Learning in Kohonen Networks Unsupervised Learning In all the forms of learning we have met so far the answer that the network is supposed to give for the training examples is known. Kohonen Networks The objective of a Kohonen network is to map input vectors (patterns) of arbitrary dimension N onto a discrete map with 1 or 2 dimensions. Learning in Kohonen Networks The learning process is as roughly as follows: initialise the weights for each output unit loop until weight changes are negligible for each input pattern present the input pattern find the winning output unit find all units in the neighbourhood of the winner update the weight vectors for all those units reduce the size of neighbourboods if required The winning output unit is simply the unit with the weight vector that has the smallest Euclidean distance to the input pattern. Demonstration Exercises
Clustering - Introduction A Tutorial on Clustering Algorithms Introduction | K-means | Fuzzy C-means | Hierarchical | Mixture of Gaussians | Links Clustering: An Introduction What is Clustering? Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. In this case we easily identify the 4 clusters into which the data can be divided; the similarity criterion is distance: two or more objects belong to the same cluster if they are “close” according to a given distance (in this case geometrical distance). The Goals of Clustering So, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Possible Applications Clustering algorithms can be applied in many fields, for instance: Requirements The main requirements that a clustering algorithm should satisfy are: Problems There are a number of problems with clustering. Clustering Algorithms Bibliography Next page
Ashutosh Saxena - Assistant Professor - Cornell - Computer Scien See our workshop at RSS'14: Planning for Robots: Learning vs Humans. Our 5th RGB-D workshop at RSS'14: Vision vs Robotics! Our special issue on autonomous grasping and manipulation is out! Saxena's Robot Learning Lab projects were featured in BBC World News. Daily Beast comments about Amazon's predictive delivery and Saxena's predictive robots. Zhaoyin Jia's paper on physics-based reasoning for RGB-D image segmentation, an oral at CVPR'13, is now conditionally accepted in IEEE TPAMI. Vaibhav Aggarwal was awarded ELI'14 research award for his work with Ashesh Jain. Koppula's video on reactive robotic response was the finalist for best video award at IROS 2013. Ashesh Jain's NIPS'13 paper on learning preferences in trajectories was mentioned in Discovery Channel Daily Planet, Techcrunch, FOX News, NBC News and several others. Saxena gave invited talks at the AI-based Robotics, at the Caging for manipulation, and at the Developmental and Social Robotics workshops at IROS 2013. Prof. Prof. Prof.
Carte auto adaptative Un article de Wikipédia, l'encyclopédie libre. Les cartes auto adaptatives ou auto organisatrices forment une classe de réseau de neurones artificiels fondée sur des méthodes d'apprentissage non-supervisées. On les désigne souvent par le terme anglais self organizing maps (SOM), ou encore cartes de Teuvo KohonenTeuvo Kohonen du nom du statisticien ayant développé le concept en 1984. Elles sont utilisées pour cartographier un espace réel, c'est-à-dire pour étudier la répartition de données dans un espace à grande dimension. Idée de base[modifier | modifier le code] Ces structures intelligentes de représentation de données sont inspirées, comme beaucoup d’autres créations de l’intelligence artificielle, par la biologie ; Il s'agit de reproduire le principe neuronal du cerveau des vertébrés : des stimuli de même nature excitent une région du cerveau bien particulière. Techniquement, la carte réalise une quantification vectorielle de l'espace de données. un neurone est le plus proche de v. . où
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. A modelm_i is associated with each grid node ( Figure 1). Like a codebook vector in vector quantization, the model is then usually a certain weighted local average of the given data items in the data space. The SOM was originally developed for the visualization of distributions of metric vectors, such as ordered sets of measurement values or statistical attributes, but it can be shown that a SOM-type mapping can be defined for any data items, the mutual pairwise distances of which can be defined. History Mathematical definition of the SOM
An approach to overcome the limits of K-means Time ago, I posted a banal case to show the limits of K-mean clustering. A follower gave us a grid of different clustering techniques (calling internal routines of Mathematica) to solve the case discussed. As you know, I like write by myself the algorithms and I like show alternative paths, so I've decided to explain a powerful clustering algorithm based on the SVM. To understand the theory behind of SVC (support vector clustering) I strongly recommend to have a look at: . In this paper you will find all technical details explained with extremely clearness. As usual I leave the theory to the books and I jump into the pragmatism of the real world. In the former image, after the statement "param x: 1 2 3 :=" there are the list of 3D points belonging to our data set. One of the characteristics of SVC is the vector notation: it allows to work with high dimensions without changes in the development of the algorithm.
Latent Dirichlet allocation In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1] Topics in LDA[edit] In LDA, each document may be viewed as a mixture of various topics. For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. Each document is assumed to be characterized by a particular set of topics. Model[edit] With plate notation, the dependencies among the many variables can be captured concisely. is the topic distribution for document i, The 1. , where .
Effet Zeigarnik Un article de Wikipédia, l'encyclopédie libre. L’effet Zeigarnik désigne la tendance à mieux se rappeler une tâche qu'on a réalisée si celle-ci a été interrompue alors qu'on cherche par ailleurs à la terminer. Le fait de s'engager dans la réalisation d'une tâche crée une motivation d'achèvement qui resterait insatisfaite si la tâche est interrompue. L'expérimentation princeps[modifier | modifier le code] Bluma Zeigarnik, élève de Kurt Lewin demande à des enfants d’accomplir, en une journée, une série de vingt petits travaux (modeler des animaux, enfiler des perles, assembler les pièces d’un puzzle…). L'inspiration de Madame Zeigarnik tirait sa source de la théorie de la psychologie de la forme (gestalt théorie). Autres données expérimentales[modifier | modifier le code] L'effet Zeigarnik est corrélé à la motivation : plus grande est la tendance personnelle à une forte motivation, plus l'effet est fort. Psychanalyse[modifier | modifier le code] Bibliographie[modifier | modifier le code]
Wikipedia 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]