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Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...
Encog is a machine learning framework available for Java and .Net. [1] 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.
Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...
Martin Riedmiller developed three algorithms, all named RPROP. Igel and Hüsken assigned names to them and added a new variant: [2] [3] RPROP+ is defined at A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm.
Persist (Java tool) Pointer (computer programming) Polymorphism (computer science) Population-based incremental learning; Prepared statement; Producer–consumer problem; Project Valhalla (Java language) Prototype pattern; Proxy pattern
Similar ideas have been used in feed-forward neural networks for unsupervised pre-training to structure a neural network, making it first learn generally useful feature detectors. Then the network is trained further by supervised backpropagation to classify labeled data.
The transaction context format used for propagation is protocol dependent and must be negotiated between the client and server hosts. For example, if the transaction manager is an implementation of the JTS specification, it will use the transaction context propagation format as specified in the CORBA OTS 1.1 specification. Transaction ...
Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks.It is a type of supervised learning.