Recommendation’s Engine based on Spread Activation algorithm « Álvaro Brange’s Blog. September 7, 2010 Suggestion graph made with test application Hi, Since last year that I haven’t added any post on my blog, but I would like add new posts. Here is the abstract: Nowadays people are reproducing the social network from your real life into a virtual space in which are represented the same social structures and relations of friendship, work, academic partners, and “love- relationships”. Read full document Regards, Álvaro Like this: Like Loading... Nearest neighbor search Nearest neighbor search (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest (or most similar) points. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set S of points in a space M and a query point q ∈ M, find the closest point in S to q. Donald Knuth in vol. 3 of The Art of Computer Programming (1973) called it the post-office problem, referring to an application of assigning to a residence the nearest post office. A direct generalization of this problem is a k-NN search, where we need to find the k closest points. Most commonly M is a metric space and dissimilarity is expressed as a distance metric, which is symmetric and satisfies the triangle inequality. Applications[edit] Methods[edit] Various solutions to the NNS problem have been proposed. .
Boltzmann machine A graphical representation of an example Boltzmann machine. Each undirected edge represents dependency. In this example there are 3 hidden units and 4 visible units. This is not a restricted Boltzmann machine. A Boltzmann machine is a type of stochastic recurrent neural network invented by Geoffrey Hinton and Terry Sejnowski in 1985. They are named after the Boltzmann distribution in statistical mechanics, which is used in their sampling function. Structure[edit] A graphical representation of a Boltzmann machine with a few weights labeled. . A Boltzmann machine, like a Hopfield network, is a network of units with an "energy" defined for the network. , in a Boltzmann machine is identical in form to that of a Hopfield network: Where: is the connection strength between unit and unit . is the state, , of unit . is the bias of unit in the global energy function. ( is the activation threshold for the unit.) The connections in a Boltzmann machine have two restrictions: . where . -th unit is on. ).
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Programming Collective Intelligence - O'Reilly Media About me and why I read this book I've been programming professionally for ~7.5 years, mainly business applications and reporting, so I already have quite some love for data. While I haven't used math much in my day jobs, I liked (and was good at) it in high school, including taking extra classes - so I have learned basic statistics. Refreshing and advancing my data analytics skills is one of my goals this year, and reading this book was part of the plan. About the book The book introduces lots of algorithms that can be used to gain new insight into any kind of data one might come across. Each of the algorithms is illustrated with real world application examples, and examples where applying them doesn't make sense are brought too. In addition to the well written, gradual introduction, the book has a concise algorithm reference at the end, so when one needs a quick refresher, there is no need to wade through the lengthy tutorials. The book was written in 2007, but is not dated.
BOOKS TOOLBOX: 50+ Sites for Book Lovers k-nearest neighbor algorithm Non-parametric classification method In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Hodges in 1951,[1] and later expanded by Thomas Cover.[2] Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. The k-NN algorithm can also be generalized for regression. For both classification and regression, a useful technique can be to assign weights to the contributions of the neighbors, so that nearer neighbors contribute more to the average than distant ones. The input consists of the k closest training examples in a data set. Statistical setting [edit] for ). on , let
Challenges in Building Large-Scale Information Retrieval Systems Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires making complex design tradeoffs in a number of dimensions, including (a) the number of user queries that must be handled per second and the response latency to these requests, (b) the number and size of various corpora that are searched, (c) the latency and frequency with which documents are updated or added to the corpora, and (d) the quality and cost of the ranking algorithms that are used for retrieval. In this talk I'll discuss the evolution of Google's hardware infrastructure and information retrieval systems and some of the design challenges that arise from ever-increasing demands in all of these dimensions. Finally, I'll describe some future challenges and open research problems in this area. Would you like to put a link to this lecture on your homepage?
3D 3D, el final A pesar de los éxitos, en 1953 se detiene de nuevo la evolución debido a laexcesiva complejidad de mantener el sistema de dos tiras de película; a quecualquier fallo en la sincronización daba al traste con la proyección y provocabadolores de cabeza y cansancio visual a los espectadores y a que la proyecciónen 3D no permitía utilizar la totalidad del aforo de la sala, ya que desde las butacaslaterales se perdía el efeco tridimensional.Afortunadamente, éxitos como “Kiss me Kate”, la famosa “Creature from the BlackLagoon” y “Dial M for Morder”, de Alfred Hitchcock, le dieron algo de continuidad,la llegada del Cinemascope o pantalla ancha, volvió a hacer que el 3D pasaraa segundo plano, aunque consiguió un último éxito con una película de título reivindicativo:“Revenge of the Creature”. 3D, simplificado 3D ampliado En los años ochenta, el formato IMAX supuso un nuevo despegue del cine en 3D, quehasta1995 presentaba obras de carácter divulgativo. ʻ Tiburón 3D ʼ Amityville 3D y . RealD
blinkx Blinkx (London AIM,[1] stylized as blinkx), is an Internet Media platform that connects online video viewers with publishers and distributors, using advertising to monetize those interactions. blinkx has an index of over 35 million hours of video and 800 media partnerships; 111 patents related to the site's search engine technology, which is known as CORE.[2] Founded in 2004, blinkx went public on the London Stock Exchange (AIM) in May, 2007. The company is headquartered in San Francisco, CA and London, England. Partnerships[edit] blinkx powers video search for sites such as AOL and ask.com. History[edit] Executives[edit] S. See also[edit] References[edit] External links[edit]
Trust Metric This document briefly describes the technical details of Advogato's trust metric. The basic trust metric evaluates a set of peer certificates, resulting in a set of accounts accepted. These certificates are represented as a graph, with each account as a node, and each certificate as a directed edge. The goal of the trust metric is to accept as many valid accounts as possible, while also reducing the impact of attackers. Advogato performs certification to three different levels: Apprentice, Journeyer, and Master. The computation of the trust metric is performed relative to a "seed" of trusted accounts. The core of the trust metric is a network flow operation. The remainder of this document presents the actual trust metric in more detail, as well as an argument for the security against attackers. Mapping into graph The mapping of certificates into a graph is dependent on a parameter: the certification level l. Assignment of capacities Conversion into single source, single sink Security proof