Self-Organizing Neural Networks: Recent Advances and Applications

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This enhance is a step in the direction of the quest for the open-ended evolution of problem-solving techniques Adaptive Modelling, Estimation and Fusion from Data read online. We view the web as a "virtual laboratory" and feature built a framework to aid experiments in web-based group studying. Our approach is named the neighborhood of Evolving inexperienced persons, or CEL.


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EDIT: click on the following to discover a hyperlink for the dummy creature new release code. Of a 7 case business audit, the care had privacy in your significant impression business , source: Applications and Science of read pdf Applications and Science of Neural. The network consists of connections, each connection providing the output of one neuron as an input to another neuron.

Each connection is assigned a weight that represents its relative importance. The propagation function computes the input to a neuron from the outputs of its predecessor neurons and their connections as a weighted sum. The neurons are typically organized into multiple layers, especially in deep learning. Neurons of one layer connect only to neurons of the immediately preceding and immediately following layers.

The layer that receives external data is the input layer. The layer that produces the ultimate result is the output layer. In between them are zero or more hidden layers. Single layer and unlayered networks are also used. Between two layers, multiple connection patterns are possible. They can be fully connected , with every neuron in one layer connecting to every neuron in the next layer. They can be pooling , where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer.

A hyperparameter is a parameter whose value is set before the learning process begins.

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The values of parameters are derived via learning. Examples of hyperparameters include learning rate , the number of hidden layers and batch size. For example, the size of some layers can depend on the overall number of layers. Learning is the adaptation of the network to better handle a task by considering sample observations. Learning involves adjusting the weights and optional thresholds of the network to improve the accuracy of the result. This is done by minimizing the observed errors.

Learning is complete when examining additional observations does not usefully reduce the error rate. Even after learning, the error rate typically does not reach 0. If after learning, the error rates too high, the network typically must be redesigned. Practically this is done by defining a cost function that is evaluated periodically during learning. As long as its output continues to decline, learning continues.

The cost is frequently defined as a statistic whose value can only be approximated. The outputs are actually numbers, so when the error is low, the difference between the output almost certainly a cat and the correct answer cat is small. Learning attempts to reduce the total of the differences across the observations. The learning rate defines the size of the corrective steps that the model takes to adjust for errors in each observation. A high learning rate shortens the training time, but with lower ultimate accuracy, while a lower learning rate takes longer, but with the potential for greater accuracy.

Optimizations such as Quickprop are primarily aimed at speeding up error minimization, while other improvements mainly try to increase reliability.

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In order to avoid oscillation inside the network such as alternating connection weights, and to improve the rate of convergence, refinements use an adaptive learning rate that increases or decreases as appropriate. A momentum close to 0 emphasizes the gradient, while a value close to 1 emphasizes the last change. While it is possible to define a cost function ad hoc , frequently the choice is determined by the functions desirable properties such as convexity or because it arises from the model e.


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  • Backpropagation is a method to adjust the connection weights to compensate for each error found during learning. The error amount is effectively divided among the connections. Technically, backprop calculates the gradient the derivative of the cost function associated with a given state with respect to the weights.

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    The weight updates can be done via stochastic gradient descent or other methods, such as Extreme Learning Machines , [45] "No-prop" networks, [46] training without backtracking, [47] "weightless" networks, [48] [49] and non-connectionist neural networks. The three major learning paradigms are supervised learning , unsupervised learning and reinforcement learning.

    They each correspond to a particular learning task. Supervised learning uses a set of paired inputs and desired outputs.

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    The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions. Tasks suited for supervised learning are pattern recognition also known as classification and regression also known as function approximation. Supervised learning is also applicable to sequential data e. This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far. The cost function is dependent on the task the model domain and any a priori assumptions the implicit properties of the model, its parameters and the observed variables.

    The cost function can be much more complicated. Tasks that fall within the paradigm of unsupervised learning are in general estimation problems; the applications include clustering , the estimation of statistical distributions , compression and filtering. In applications such as playing video games, an actor takes a string of actions, receiving a generally unpredictable response from the environment after each one.

    The goal is to win the game, i. In reinforcement learning , the aim is to weight the network devise a policy to perform actions that minimize long-term expected cumulative cost.

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    The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly. Formally the environment is modeled as a Markov decision process MDP with states s 1 ,. Taken together, the two define a Markov chain MC.

    The aim is to discover the lowest-cost MC. ANNs serve as the learning component in such applications. Tasks that fall within the paradigm of reinforcement learning are control problems, games and other sequential decision making tasks. In a Bayesian framework, a distribution over the set of allowed models is chosen to minimize the cost.

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    Evolutionary methods , [58] gene expression programming , [59] simulated annealing , [60] expectation-maximization , non-parametric methods and particle swarm optimization [61] are other learning algorithms. Convergent recursion is a learning algorithm for cerebellar model articulation controller CMAC neural networks. Two modes of learning are available: stochastic and batch. In stochastic learning, each input creates a weight adjustment. In batch learning weights are adjusted based on a batch of inputs, accumulating errors over the batch.

    Stochastic learning introduces "noise" into the process, using the local gradient calculated from one data point; this reduces the chance of the network getting stuck in local minima. However, batch learning typically yields a faster, more stable descent to a local minimum, since each update is performed in the direction of the batch's average error.