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Encog
Encog is a machine learning framework available for Java and .Net.J. Heaton http://www.jmlr.org/papers/volume16/heaton15a/heaton15a.pdf Encog: Library of Interchangeable Machine Learning Models for Java and C# Encog supports different learning algorithms such as Bayesian Networks, Hidden Markov Models and Support Vector Machines. However, its main strength lies in its neural network algorithms. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using many different techniques. Multithreading is used to allow optimal training performance on multicore machines. Encog can be used for many tasks, including medicalD. Heider, J. Verheyen, D. Hoffmann http://www.biomedcentral.com/content/pdf/1471-2105-11-37.pdf Predicting Bevirimat resistance of HIV-1 from genotype and financial research.J. Heaton http://www.devx.com/opensource/Article/44014/1954 Basic Market Forecasting with En ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
JOONE
JOONE (Java Object Oriented Neural Engine) is a component based neural network framework built in Java. Features Joone consists of a component-based architecture based on linkable components that can be extended to build new learning algorithms and neural networks architectures. Components are plug-in code modules that are linked to produce an information flow. New components can be added and reused. Beyond simulation, Joone also has to some extent multi-platform deployment capabilities. Joone has a GUI Editor to graphically create and test any neural network, and a distributed training environment that allows for neural networks to be trained on multiple remote machines. Comparison As of 2010, Joone, Encog and Neuroph are the major free component based neural network development environment available for the Java platform. Unlike the two other (commercial) systems that are in existence, Synapse and NeuroSolutions, it is written in Java and has direct cross-platform support. A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuroevolution Of Augmenting Topologies
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying"). Performance On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods.Kenneth O. Stanley and Risto Miikkulainen (2002). "Evolving Neural Networks Through Augmenting ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Perceptron
In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised classification, supervised learning of binary classification, binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of Weighting, weights with the feature vector. History The perceptron was invented in 1943 by McCulloch and Pitts. The first implementation was a machine built in 1958 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, funded by the United States Office of Naval Research. The perceptron was intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it was subsequently implemented in custom-built hardware as the "Mark 1 perceptron". This machine was de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
FANN
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Fann, or FANN, may refer to: *Fast Artificial Neural Network *Fann Wong use both this parameter and , birth_date to display the person's date of birth, date of death, and age at death) --> , death_place = , death_cause = , body_discovered = , resting_place = , resting_place_coordinat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems 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 po ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Competitive Learning
Competitive learning is a form of unsupervised learning in artificial neural networks Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ..., in which nodes compete for the right to respond to a subset of the input data. A variant of Hebbian learning, competitive learning works by increasing the specialization of each node in the network. It is well suited to finding clusters within data. Models and algorithms based on the principle of competitive learning include vector quantization and self-organizing maps (Kohonen maps). Principles There are three basic elements to a competitive learning rule: * A set of neurons that are all the same except for some randomly distributed synaptic weights, and which therefore respond differently to a given set of input patterns * A limit impo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Manhattan Update Rule Propagation
Manhattan (), known regionally as the City, is the most densely populated and geographically smallest of the five boroughs of New York City. The borough is also coextensive with New York County, one of the original counties of the U.S. state of New York. Located near the southern tip of New York State, Manhattan is based in the Eastern Time Zone and constitutes both the geographical and demographic center of the Northeast megalopolis and the urban core of the New York metropolitan area, the largest metropolitan area in the world by urban landmass. Over 58 million people live within 250 miles of Manhattan, which serves as New York City’s economic and administrative center, cultural identifier, and the city’s historical birthplace. Manhattan has been described as the cultural, financial, media, and entertainment capital of the world, is considered a safe haven for global real estate investors, and hosts the United Nations headquarters. New York City is the headquarters of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Levenberg–Marquardt Algorithm
In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting. The LMA interpolates between the Gauss–Newton algorithm (GNA) and the method of gradient descent. The LMA is more robust than the GNA, which means that in many cases it finds a solution even if it starts very far off the final minimum. For well-behaved functions and reasonable starting parameters, the LMA tends to be slower than the GNA. LMA can also be viewed as Gauss–Newton using a trust region approach. The algorithm was first published in 1944 by Kenneth Levenberg, while working at the Frankford Army Arsenal. It was rediscovered in 1963 by Donald Marquardt, who worked as a statistician at DuPont, and independently by Girard, Wynne and Morrison. The LMA is used in many software applications for solvin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Conjugate Gradient Method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-definite. The conjugate gradient method is often implemented as an iterative algorithm, applicable to sparse systems that are too large to be handled by a direct implementation or other direct methods such as the Cholesky decomposition. Large sparse systems often arise when numerically solving partial differential equations or optimization problems. The conjugate gradient method can also be used to solve unconstrained optimization problems such as energy minimization. It is commonly attributed to Magnus Hestenes and Eduard Stiefel, who programmed it on the Z4, and extensively researched it. The biconjugate gradient method provides a generalization to non-symmetric matrices. Various nonlinear conjugate gradient methods seek minima of nonlinear optimization problems. Description of the problem address ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rprop
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. Similarly to the Manhattan update rule, Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight". For each weight, if there was a sign change of the partial derivative of the total error function compared to the last iteration, the update value for that weight is multiplied by a factor ''η''−, where ''η''− 1. The update values are calculated for each weight in the above manner, and finally each weight is changed by its own update value, in the opposite direction of that weight's partial derivative, so as to minimise the total error function. ''η''+ is empirically set to 1.2 and ''η''− to 0.5. RPROP is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Backpropagation
In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward artificial neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally. These classes of algorithms are all referred to generically as "backpropagation". In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. This efficiency makes it feasible to use gradient methods for training multilayer networks, updating weights to minimize loss; gradient descent, or variants such as stochastic gradient descent, are commonly used. The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one laye ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Self-organizing Map
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example, a data set with p variables measured in n observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a two-dimensional "map" such that observations in proximal clusters have more similar values than observations in distal clusters. This can make high-dimensional data easier to visualize and analyze. An SOM is a type of artificial neural network but is trained using competitive learning rather than the error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. The SOM was introduced by the Finnish professor Teuvo Kohonen in the 1980s and therefore is som ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
