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.
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. 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. Laterale Umfeldhemmung[Bearbeiten] Struktur und Lernen[Bearbeiten]
Clustering - Introduction A Tutorial on Clustering Algorithms Introduction | K-means | Fuzzy C-means | Hierarchical | Mixture of Gaussians | Links Clustering: An Introduction What is Clustering? 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 Classification Clustering algorithms may be classified as listed below: Exclusive Clustering Overlapping Clustering Hierarchical Clustering Probabilistic Clustering where d is the dimensionality of the data.
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.
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
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 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. 3D problem Just to show the same algorithm working in 3D on the same problem: And Here you are the SVC results plotted by Mathematica:
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 .
CRF Project Page ls | About John Lafferty My research is in machine learning and statistics, with basic research on theory, methods, and algorithms. Areas of focus include nonparametric methods, sparsity, the analysis of high-dimensional data, graphical models, information theory, and applications in language processing, computer vision, and information retrieval. Perspectives on several research topics in statistical machine learning appeared in this Statistica Sinica commentary. This work has received support from NSF, ARDA, DARPA, AFOSR, and Google. Rodeo: Sparse, greedy, nonparametric regression with Larry WassermanAnn. Most methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality.
Amos Storkey - Research - Belief Networks Belief Networks and Probabilistic Graphical Models Belief networks (Bayes Nets, Bayesian Networks) are a vital tool in probabilistic modelling and Bayesian methods. They are one class of probabilistic graphical model. Use of belief networks has become widespread partly because of their intuitive appeal. Introduction to Bayesian Methods Although belief networks are a tool of probability theory, their most common use is within the framework of Bayesian analysis. In order to infer anything from data, we must have and use prior information. One implication of these beliefs is that there is no indisputable way of obtaining knowledge from data. The Bayesian approach to problems can be summed up in this simple way: The big issues in Bayesian methods involve how to find out what form P(Q,D) should take, and how to calculate P(Q|D). Belief Networks Belief Networks use a graphical representation to represent probabilistic structure. A belief network is a directed graph. Inference in Belief Networks
Pareto principle The Pareto Principle asserts that only a "vital few" peapods produce the majority of peas. The Pareto principle (also known as the 80/20 rule, the law of the vital few, or the principle of factor sparsity)[1][2] states that, for many events, roughly 80% of the effects come from 20% of the causes.[3] Management consultant Joseph M. Juran suggested the principle and named it after Italian economist Vilfredo Pareto, who noted the 80/20 connection while at the University of Lausanne in 1896, as published in his first work, Cours d'économie politique. Essentially, Pareto showed that approximately 80% of the land in Italy was owned by 20% of the population. It is an axiom of business management that "80% of sales come from 20% of clients".[4] Richard Koch authored the book, The 80/20 Principle, which illustrated some practical applications of the Pareto principle in business management and life.[5] The Pareto principle is only tangentially related to Pareto efficiency. In economics[edit]