AdaBoost While every learning algorithm will tend to suit some problem types better than others, and will typically have many different parameters and configurations to be adjusted before achieving optimal performance on a dataset, AdaBoost (with decision trees as the weak learners) is often referred to as the best out-of-the-box classifier. When used with decision tree learning, information gathered at each stage of the AdaBoost algorithm about the relative 'hardness' of each training sample is fed into the tree growing algorithm such that later trees tend to focus on harder to classify examples. Overview[edit] Training[edit] AdaBoost refers to a particular method of training a boosted classifier. where each is a weak learner that takes an object as input and returns a real valued result indicating the class of the object. -layer classifier will be positive if the sample is believed to be in the positive class and negative otherwise. Each weak learner produces an output, hypothesis of the resulting .
Markov blanket In a Bayesian network, the Markov blanket of node A includes its parents, children and the other parents of all of its children. in a Bayesian network is the set of nodes composed of 's parents, its children, and its children's other parents. In a Markov network, the Markov blanket of a node is its set of neighboring nodes. A Markov blanket may also be denoted by Every set of nodes in the network is conditionally independent of when conditioned on the set , that is, when conditioned on the Markov blanket of the node . and The Markov blanket of a node contains all the variables that shield the node from the rest of the network. In a Bayesian network, the values of the parents and children of a node evidently give information about that node; however, its children's parents also have to be included, because they can be used to explain away the node in question. See also[edit] Moral graph Notes[edit] Jump up ^ Pearl, Judea (1988).
Kernel Methods for Pattern Analysis - The Book 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
Principal component analysis PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the corresponding eigenvalue, and shifted so their tails are at the mean. Principal component analysis (PCA) is a statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. PCA is closely related to factor analysis. PCA is also related to canonical correlation analysis (CCA). Details[edit] Mathematically, the transformation is defined by a set of p-dimensional vectors of weights or loadings that map each row vector of X to a new vector of principal component scores , given by First component[edit] The first loading vector w(1) thus has to satisfy Further components[edit] Covariances[edit]
Eigenvalues and eigenvectors In this shear mapping the red arrow changes direction but the blue arrow does not. The blue arrow is an eigenvector of this shear mapping, and since its length is unchanged its eigenvalue is 1. An eigenvector of a square matrix that, when the matrix is multiplied by , yields a constant multiple of , the multiplier being commonly denoted by . (Because this equation uses post-multiplication by , it describes a right eigenvector.) The number is called the eigenvalue of corresponding to In analytic geometry, for example, a three-element vector may be seen as an arrow in three-dimensional space starting at the origin. is an arrow whose direction is either preserved or exactly reversed after multiplication by . is an eigenfunction of the derivative operator " ", with eigenvalue , since its derivative is is the set of all eigenvectors with the same eigenvalue, together with the zero vector.[1] An eigenbasis for . Eigenvalues and eigenvectors have many applications in both pure and applied mathematics. and or
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.
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,
Empirical risk minimization Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on the performance of learning algorithms. Background[edit] Consider the following situation, which is a general setting of many supervised learning problems. and and would like to learn a function (often called hypothesis) which outputs an object , given . where is an input and is the corresponding response that we wish to get from To put it more formally, we assume that there is a joint probability distribution over , and that the training set consists of instances drawn i.i.d. from . is not a deterministic function of , but rather a random variable with conditional distribution for a fixed We also assume that we are given a non-negative real-valued loss function which measures how different the prediction of a hypothesis is from the true outcome is then defined as the expectation of the loss function: , where is the indicator notation.
PubMed Central, Figure 1: AMIA Annu Symp Proc. 2003; 2003: 21–25. 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
Evolutionary Algorithms This grasshopper definition was made when the Galapagos evolutionary solver was released for GH. The inspiration for this simple approach on evolutionary algorithms was drawn by a genetic algorithm I have written in Processing, that I might share later on. It is a fairly simple way to understand how Genetic Algorithms perform an how this can be implemented through GH. To download visit the [Sub]Code page. Decision tree Traditionally, decision trees have been created manually. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. Overview[edit] A decision tree is a flowchart-like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label (decision taken after computing all attributes). In decision analysis a decision tree and the closely related influence diagram is used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated. A decision tree consists of 3 types of nodes: Decision tree building blocks[edit] See also[edit]
Visualizious: Visualizing Social Indexing Visualizious Visualizious is a research project about social indexing (a.k.a. social tagging), information retrieval and visualization. The project is carried out by Yusef Hassan Montero and Víctor Herrero Solana (University of Granada, Spain). Visualizing Social Indexing Semantics This prototype allows visualizing both the overview and detail of semantic relationships intrinsic in the folksonomy. Screenshots (click to enlarge) Related papers Hassan-Montero, Y.; Herrero-Solana, V. (2007) Visualizing Social Indexing Semantics. Improved Tag-Clouds Tag-Cloud is a simple and widely used visual interface model, but with some restrictions that limit its utility as visual information retrieval interface. Our work presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. Screenshot (click to enlarge) Related papers Previous Research (in spanish)