gradient descent training algorithm
HANDWRITTEN NUMERAL PATTERN RECOGNITION TECHNIQUES: REVIEW PAPER
... steepest descent, the learning rate is held constant throughout ...the algorithm is very sensitive to the proper setting of the learning ...the algorithm can oscillate and become ...the ...
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A FASTER ESTIMATION ALGORITHM APPLIED TO POWER QUALITY PROBLEMS
... significant training. We have worked in improving the training of weights of Neural Network by using Particle Swarm Optimization (PSO) combined with Gradient Descent (GD) which results in ...
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Handwritten Character Recognition using Modified Gradient Descent Technique of Neural Networks and Representation of Conjugate Descent for Training Patterns
... neural network system that consists of 150 neurons in input layer, 10 neurons in hidden layer and 26 output neurons. The network has 150 input neurons that are equivalent to the input character’s size as we have resized ...
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Detection of Mobile Keyloggers Using Deep Learning
... This algorithm is a technique that is often preferred as it uses a combination of stochastic gradient descent and batch gradient ...the training set into small lots and changes each of ...
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The effect of adaptive gain and adaptive momentum in improving training time of Gradient Descent back propagation algorithm on classification problems
... of training examples is batch ...propagation algorithm based on extrapolation of each individual interconnection ...propagation algorithm is individually ...conjugate gradient algorithm ...
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Modelling of direct metal laser sintering of EOS DM20 bronze using neural networks and genetic algorithms
... different training algorithms, namely traingd (Gradient descent), traingdm (Gradient descent with momentum), traingdx (Gradient descent momentum with an adaptive learning ...
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The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems
... propagation algorithm is widely implemented in the most practical neural networks applications and performed relatively well, this algorithm still needs some ...working algorithm proposed by Nazri ...
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Performance Evaluation of Neural Network based Cognitive eNodeB in LTE Uplink
... with gradient descent (GD) and Levenberg Marquardt (LM) training algorithm based framework is used to improve inter cell interference coordination (ICIC) and radio resource management (RRM) in ...
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Optimal Parameter Selection Using Three-term Back Propagation Algorithm for Data Classification
... the training time without modifying the network ...proposed algorithm, known as, (BPGD-A3T), presents better convergence rate and can avoid the network from trapping into local ...proposed algorithm ...
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Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell
... CG algorithm to converge quickly (Table 1) compared to ...LM algorithm presents the best behavior of the convergence compared to other algorithms, due to the combination between the features of SD and ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... (BP) training algorithm is a renowned representative of all iterative gradient descent algorithms used for supervised learning in neural ...learning algorithm have been reported to beat ...
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Optical Character Recognition using Neural Network
... Different training algorithms have implemented and the performance of these algorithms is ...propagation algorithm is used to train the multi layered feed forward neural ...network. Gradient ...
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The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks
... BP algorithm implements the gradient descent method which is the most venerable, but also one of the least effective, classical optimisation ...batch training method, weight changes are ...
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Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
... Stochastic Gradient Descent (SGD) is such an algorithm and it is an attractive choice for online Support Vector Machine (SVM) training due to its simplicity and ...SVM training which ...
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Mill Load Control System Based on the Improved Ant Colony Neural Network
... using gradient descent algorithm such back propagation algorithm (BP) with a longer convergence time and more probability to fall into local ...colony algorithm, as a global ...
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Multi Valued Neuron with Sigmoid Activation Function for Pattern Classification
... Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-va- lued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation ...
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Accelerated Mann and CQ algorithms for finding a fixed point of a nonexpansive mapping
... [, ], and the CQ method [] are useful fixed point algorithms to solve the fixed point problems. Meanwhile, to guarantee practical systems and networks (see, e.g., [–]) are stable and reliable, the fixed point has to ...
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An Efficient Algorithm to Analyse the Cost Sensitive Measures on Datasets
... CBGSA algorithm that may achieve best sensitivity, specificity, and misclassification result when compare to CSOGD algorithm and also to reduce the time ...
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Multi variable geometry repair and optimization of passive vibration isolators
... repair algorithm, and also that there is constant improvement throughout the history of the ...a gradient descent method, we cannot be assured of a successful repair – there may be a minima of ε in ...
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On the Equivalence of Holographic and Complex Embeddings for Link Prediction
... We show the equivalence of two state- of-the-art models for link prediction/ knowledge graph completion: Nickel et al’s holographic embeddings and Trouil- lon et al.’s complex embeddings. We first consider a spectral ...
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