gradient-based learning algorithms
A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms
... machine learning classification ...classification algorithms: simple logistic Regression learning, stochastic gradient descent learning and multilayer perceptron network, random ...
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Robust gradient-based discrete-time iterative learning control algorithms
... 1. The inverse-model-based algorithm suffers from substantial increases (around 10-fold in mag- nitude) in the mean square error in iterations 5 − 10. This is regarded as a substantial overall degradation in ...
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Indian stock market prediction using artificial neural networks on tick data
... Network learning algorithms, ...Conjugate Gradient and Bayesian Regularization by predicting over tick by tick dataset and 15-min ...is based on Levenberg-Marquardt optimization which uses an ...
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Autonomous toolkit to forecast customer churn
... machine learning algorithms which applied to the challenging problem of the customer churn in the telecom ...company based dataset of BigML ...machine learning algorithms ...
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Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
... of algorithms to perform learning tasks is based on iterative procedure (Engl et ...of algorithms, an empirical objective function is optimized in an iter- ative way with no explicit ...
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Extending the inferential capabilities of model-based gradient boosting algorithms
... In this paper, we propose a new method to determine the optimal number of iterations in model-based boosting for variable selection inspired by probing, a method frequently used in related areas of machine ...
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Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems
... online learning systems that combine gradient-based rule evalu- ation with evolutionary-based rule structuring ...online learning methods that are applica- ble to datamining, ...
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Earthquake Prediction System by LSTM
... algorithm based on mathematical analysis, machine learningalgorithms like decision trees and support vector machines, and precursors ...systemdeep learning technique called long short-term memory with ...
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Real Estate Investment Advising Using Machine Learning
... Machine Learning algorithms namely Linear Regression using gradient descent, K nearest neighbor regression and Random forest regression for prediction of real estate price ...machine learning ...
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Comparative Study of Sentiment Analysis with Product Reviews Using Machine Learning and Lexicon-Based Approaches
... machine learning approach: SVM, Gradient Boosting, and LR algorithms and three lexicon-based techniques: VADER, Pattern, and SentiWordNet lexicons to analyze Amazon reviews ...machine ...
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Two Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate
... novel learning rate based on the gradient reviation of learning rate, and in the second one, learning performance and the number of training iterations were combined with the aim of ...
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Cost-sensitive online classification
... online learning have been studied extensively in data mining and machine learning communities, ...online gradient descent techniques. Based on the framework, we propose a family of ...
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Properties of the sign gradient descent algorithms
... optimization algorithms [12, ...the gradient and is used in many applications as optimal control [2], video coding [33], localization [19] or robotics ...fast gradient method is developed by Nesterov ...
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Predicting Attendance at Major League Soccer Matches: A Comparison of Four Techniques
... (eXtreme Gradient Boosting) is one of the most loved machine learning algorithms at ...[machine learning] competitions. It can be used for supervised learning tasks such as Regression, ...
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Simple and Fast Calculation of the Second-Order Gradients for Globalized Dual Heuristic Dynamic Programming in Neural Networks
... Each iteration of training consisted of the application of these two weight updates accumulated over all non-terminal time steps of the trajectory, t ∈ {0, 1}. Each trajectory started from x = 0.8. Experimental results, ...
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On Perturbed Proximal Gradient Algorithms
... The key property to study the behavior of the sequence the perturbed proximal gradient algorithm is the following elementary lemma which might be seen as a deter- ministic version of the Robbins-Siegmund lemma ...
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Lazifying Conditional Gradient Algorithms
... Vanilla Frank–Wolfe Method We tested the vanilla Frank–Wolfe algorithm on the six video colocalization instances with underlying path polytopes from http://lime.cs.elte. hu/~kpeter/data/mcf/netgen/ (Figure 1). In these ...
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Prediction of Student’s Performance based on Incremental Learning
... online learning whereby each training sample is examined only ...online learning is necessary instead of batch ...incremental learning for achieving better ...
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Nonlinear parameter estimation in classification problems
... One shortcoming of all gradient descent type algorithms, such as the online learning algorithm discussed in the first part of this thesis, is that estimates may be attracted to local min[r] ...
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Metaheuristic Techniques for Conformational Search
... Metaheuristic techniques have been constantly used in solving CS problems. These population-based probabilistic techniques explore conformational space by random perturbation of atomic Cartesian coordinates or the ...
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