# Sebastian Thrun's Homepage

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Factor graph In probability theory and its applications, a factor graph is a particular type of graphical model, with applications in Bayesian inference, that enables efficient computation of marginal distributions through the sum-product algorithm. One of the important success stories of factor graphs and the sum-product algorithm is the decoding of capacity-approaching error-correcting codes, such as LDPC and turbo codes. A factor graph is an example of a hypergraph, in that an arrow (i.e., a factor node) can connect more than one (normal) node. When there are no free variables, the factor graph of a function f is equivalent to the constraint graph of f, which is an instance to a constraint satisfaction problem. Definition A factor graph is a bipartite graph representing the factorization of a function. where , the corresponding factor graph consists of variable vertices , and edges . and variable vertex when . , such as the marginal distributions. Examples An example factor graph is defined as

Data Mining: Finding Similar Items and Users Because we want to give kick-ass product recommendations. I'm showing you how to find related items based on a really simple formula. If you pay attention, this technique is used all over the web (like on Amazon) to personalize the user experience and increase conversion rates. To get one question out of the way: there are already many available libraries that do this, but as you'll see there are multiple ways of skinning the cat and you won't be able to pick the right one without understanding the process, at least intuitively. Defining the Problem To find similar items to a certain item, you've got to first define what it means for 2 items to be similar and this depends on the problem you're trying to solve: In each case you need a way to classify these items you're comparing, whether it is tags, or items purchased, or movies reviewed. Redefining the Problem in Terms of Geometry We'll be using my blog as sample. ["API", "Algorithms", "Amazon", "Android", "Books", "Browser"] That's 6 tags.

Bucket - XKCD Wiki Bucket has an outer shell of metal[citation needed]; within the metal is a protective layer of high density plastic[citation needed], in which may or may not reside pure HOH[citation needed]. There[citation needed] can only be speculation about what else the Bucket contains.[citation needed] Do not make our Bucket stupid or mean. Any stupiding of the Bucket will get you warned, kicked, and then banned.  Installing Download the source files from or using git, mirror the repository from here: \$ wget \$ wget \$ wget \$ wget Setup a database (MySQL recommended) - for example, on debian or ubuntu: \$ sudo apt-get install mysql-server Create the tables described in bucket.sql. \$ . People

Intelligent Autonomous Systems - Home 5 of the Best Free and Open Source Data Mining Software The process of extracting patterns from data is called data mining. It is recognized as an essential tool by modern business since it is able to convert data into business intelligence thus giving an informational edge. At present, it is widely used in profiling practices, like surveillance, marketing, scientific discovery, and fraud detection. There are four kinds of tasks that are normally involve in Data mining: * Classification - the task of generalizing familiar structure to employ to new data* Clustering - the task of finding groups and structures in the data that are in some way or another the same, without using noted structures in the data.* Association rule learning - Looks for relationships between variables.* Regression - Aims to find a function that models the data with the slightest error. For those of you who are looking for some data mining tools, here are five of the best open-source data mining software that you could get for free: Orange RapidMiner Weka JHepWork

5 de los mejores software de minería de datos de Código Libre y Abierto | El rincón de JMACOE El proceso de extracción de patrones a partir de datos se llama minería de datos. Es reconocida como una herramienta esencial de los negocios modernos, ya que es capaz de convertir los datos en inteligencia de negocios dando así una ventaja de información. Actualmente, es ampliamente utilizado en las prácticas de perfil, como vigilancia, comercialización, descubrimientos científicos, y detección de fraudes. Hay cuatro tipos de tareas que normalmente se involucran en la minería de datos:Clasificación – la tarea de generalizar una estructura familiar para utilizarla en los nuevos datosAgrupamiento – la tarea de encontrar grupos y estructuras en los datos que son de alguna manera u otra lo mismo, sin necesidad de utilizar las estructuras observadas en los datos.Aprendizaje de reglas de asociación – Busca relaciones entre las variables.Regresión – Su objetivo es encontrar una función que modele los datos con el menor error. Orange RapidMiner JHepWork