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Support vector machine

Support vector machine
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. Definition[edit] Whereas the original problem may be stated in a finite dimensional space, it often happens that the sets to discriminate are not linearly separable in that space. Note that if . belongs. . Related:  Machine Learning

Kernel Methods for Pattern Analysis - The Book TUTO : ZIPABOX, Latitude et géolocalisation 2.1 Réédité le 30/03/13 puis le 3/04/13 Salut à tous ! Après vous avoir proposé une solution pour déclencher des scénarios à distance, aujourd’hui je vous propose un peu la même chose, mais avec un gros avantage, c’est que cette fois ci ma solution est universelle (IOS, ANDROID, Blackberry..) !!! De plus, contrairement à la première solution que je vous ai donnée, celle ci envoie la distance vous séparant de votre maison à la Zipabox. C’est bien plus complet, puisque nous pourrons lancer différents scénario à déclencher en fonction de notre distance. Ça nécessite au préalable d’avoir un compte Google, un smartphone et l’application généralissime Latitude. Passons à l’explication Je tenais tout d’abord à citer mes sources, puisque pour cette solution je suis reparti de celle de Cedric Loqueneu de maison-et-domotique.com que je salut au passage. Préparation de la Zipa Alors pour commencer nous allons créer un Virtual Meter : Pour ma part je l’ai appelé loc : On copie la première ligne !! <!

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] The central connectionist principle is that mental phenomena can be described by interconnected networks of simple and often uniform units. The form of the connections and the units can vary from model to model. For example, units in the network could represent neurons and the connections could represent synapses. Spreading activation[edit] In most connectionist models, networks change over time. Neural networks[edit] Most of the variety among neural network models comes from: Biological realism[edit] Learning[edit] The weights in a neural network are adjusted according to some learning rule or algorithm.

Neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] History[edit] Farley and Wesley A. Models[edit] or both

Perceptron Een perceptron (of meerlaags perceptron) is een neuraal netwerk waarin de neuronen in verschillende lagen met elkaar verbonden zijn. Een eerste laag bestaat uit ingangsneuronen, waar de inputsignalen aangelegd worden. Vervolgens zijn er één of meerdere 'verborgen’ lagen, die zorgen voor meer 'intelligentie' en ten slotte is er de uitgangslaag, die het resultaat van het perceptron weergeeft. Alle neuronen van een bepaalde laag zijn verbonden met alle neuronen van de volgende laag, zodat het ingangssignaal voort propageert door de verschillende lagen heen. Single-layer Perceptron[bewerken] De single-layer perceptron is de simpelste vorm van een neuraal netwerk, in 1958 door Rosenblatt ontworpen (ook wel Rosenblatt's perceptron genoemd). Rosenblatt's Perceptron Het is mogelijk het aantal klassen uit te breiden naar meer dan twee, wanneer de output layer wordt uitgebreid met meerdere output neurons. Trainingsalgoritme[bewerken] Begrippen: = inputvector = gewichtsvector (weights vector) Met = bias

Zibase et Géolocalisation Encouragé par Pascal, c’est non sans une certaine appréhension (et un peu de fierté ) que je vous présente mon 1er tuto ou coment envoyer à votre zibase la distance qui vous sépare de votre domicile. Elle servira à déclencher des scénarios (par exemple ouvrir le portail lorsque j’arrive à 2 km de ma maison, allumer le chauffage lorsque je suis à moins de 50 km, activer l’alarme lorsque je suis à plus d’1 km). Mais je vous présenterai cela dans un second tuto. Pour écrire ce scénario, je me suis largement inspiré des articles de Vincent Paulet (“ZIPABOX, Latitude et géolocalisation 2.1”) et de Cédric de Maison et Domotique (“script google geolocalisation et eedomus”), merci à eux. 1. 1 Zibase1 Smartphone avec l’application latitude de google (application disponible sous Android, IPhone, blackberry) : infos ICI1 compte google Nous allons créer une sonde qui collectera la distance qui vous sépare de votre domicile. Pour cela se connecter sur le configurateur de zibase.net (ou zibase-club.net).

Autoencoder An autoencoder, autoassociator or Diabolo network[1]:19 is an artificial neural network used for learning efficient codings.[2] The aim of an auto-encoder is to learn a compressed, distributed representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Overview[edit] Architecturally, the simplest form of the autoencoder is a feedforward, non-recurrent neural net that is very similar to the multilayer perceptron (MLP), with an input layer, an output layer and one or more hidden layers connecting them. The difference with the MLP is that in an autoencoder, the output layer has equally many nodes as the input layer, and instead of training it to predict some target value y given inputs x, an autoencoder is trained to reconstruct its own inputs x. I.e., the training algorithm can be summarized as For each input x, Do a feed-forward pass to compute activations at all hidden layers, then at the output layer to obtain an output x̂ Training[edit]

Géolocalisation sur l’eedomus avec un smartphone Lors du test de l’eedomus, je vous avais brièvement parlé de la possibilité d’ajouter un traceur GPS afin de suivre la position de sa voiture, ou même d’une personne. Le traceur GPS n’est pas encore disponible, en revanche, je me suis amusé un peu avec l’API de l’eedomus, et j’ai découvert un peu par hasard qu’il était possible d’envoyer des coordonnées GPS à la box via cette API, et ainsi de « simuler » un traceur GPS. La question ensuite était de savoir comment récupérer automatiquement sa position GPS pour l’envoyer à l’eedomus. J’ai fait quelques tests, je vous livre aujourd’hui ma petite méthode, qui fonctionne plutôt bien pour utiliser la géolocalisation sur l’eedomus. Il va d’abord falloir créer un device pour la géolocalisation. Ensuite, on le configure. On relève bien le code API, qui nous servira plus tard. Pour la géolocalisation, nous allons utiliser le service Latitude de Google, et une petite application Latitudie sur iPhone, ou sur Android. Vous les recevez par email.

Feedforward neural network In a feed forward network information always moves one direction; it never goes backwards. A feedforward neural network is an artificial neural network where connections between the units do not form a directed cycle. This is different from recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. Single-layer perceptron[edit] The simplest kind of neural network is a single-layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. A perceptron can be created using any values for the activated and deactivated states as long as the threshold value lies between the two. Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. (times See also[edit]

genetic algorithm Competitive algorithm for searching a problem space Methodology[edit] Optimization problems[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. Initialization[edit] Selection[edit] Genetic operators[edit] Heuristics[edit] Termination[edit] Limitations[edit] Variants[edit]

Feature learning Feature learning or representation learning[1] is a set of techniques in machine learning that learn a transformation of "raw" inputs to a representation that can be effectively exploited in a supervised learning task such as classification. Feature learning algorithms themselves may be either unsupervised or supervised, and include autoencoders,[2] dictionary learning, matrix factorization,[3] restricted Boltzmann machines[2] and various form of clustering.[2][4][5] When the feature learning can be performed in an unsupervised way, it enables a form of semisupervised learning where first, features are learned from an unlabeled dataset, which are then employed to improve performance in a supervised setting with labeled data.[6][7] Clustering as feature learning[edit] K-means clustering can be used for feature learning, by clustering an unlabeled set to produce k centroids, then using these centroids to produce k additional features for a subsequent supervised learning task. See also[edit]

figurines 8-12 ans (vérifier) Comment donner le goût des livres à un enfant ? La question taraude de nombreux parents. Mais il est parfois difficile de se repérer dans la multitude des nouveautés de l’édition de jeunesse. Et, du côté des classiques, si l’on est assuré de trouver des ouvrages ayant fait leurs preuves sur le plan littéraire, reste à repérer ceux qui vont vraiment captiver votre jeune lecteur. Voici quelques pistes pour vous aider à faire mouche. Tobie Lolness Du haut de son millimètre et demi, ce tout petit héros a déjà conquis de nombreux prix littéraires et été traduit en vingt-six langues. L’histoire de Tobie se construit comme une sorte de puzzle entre une course effrénée pour échapper à ses ennemis et de nombreux flashbacks retraçant les moments clés de la vie de l’arbre et de ses habitants. Alice au Pays des Merveilles Avec A travers le miroir, le livre de Lewis Caroll est le premier succès commercial de la littérature de jeunesse, où il a fait entrer le merveilleux et le fantastique. Le Hobbit

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. The feed-forward architecture Feed-forward networks have a characteristic layered architecture, with each layer comprising one or more simple processing units called artificial neurons or nodes. Diagram of 2-Layer Perceptron Training a feed-forward net 1. 2. 3.

Fiction, Design, and Genetic Algorithms Computational designers in architecture (and grasshopper dilettantes such as myself) love to (over)use genetic algorithms in everyday work. Genetic algorithms (or GAs, as the cool kids call them) are a particularly fancy method for optimization that work as a kind of analogy to the genetic process in real life. The parameters you're optimizing for get put into a kind of simulated chromosome and then a series of generated genotypes slowly evolve into something that more closely fits the solution you're looking for, with simulated crossover and mutation to help make sure you're getting closer to a global optimum than a local one. For those that don't regularly optimize (I know I should more often, but it's so much easier to just sit on the couch and vegetate), the imagery that gets used is of a "fitness landscape" where you're looking for the highest peak or the lowest valley, which represents the best solution to a problem: A. B. C. D. E. F. To which I responded in the comments,

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