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Genetic Programming with Gradient Descent Search for Multiclass Object Classification [chapter]

Mengjie Zhang, Will Smart
2004 Lecture Notes in Computer Science  
This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems.  ...  This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems.  ...  We will also investigate the power and reliability of the gradient descent GP methods on even more difficult image classification problems such as face recognition problems and satellite image detection  ... 
doi:10.1007/978-3-540-24650-3_38 fatcat:rehqevsetvgx3fmvjzagk7mrqq

Multiclass Classifier Designed Using Stepwise Crossover and Special Mutation Technique

Pankaj Patidar
2012 Advanced Computing An International Journal  
A Multiclass classifier is an approach for designing classifiers for a m-class (m>=2) problem using genetic programming (GP).  ...  To demonstrate our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets.  ...  image data sets with object classification problems of increasing difficulty.  ... 
doi:10.5121/acij.2012.3110 fatcat:2xqx4kbdkvhxvfekrqhv6vmnjy

Automated Machine Learning in Practice: State of the Art and Recent Results

Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lorwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann
2019 2019 6th Swiss Conference on Data Science (SDS)  
Thus, there is an ever growing demand in work force with the necessary skill set to do so.  ...  This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML.  ...  ACKNOWLEDGEMENT We are grateful for support by Innosuisse grant 25948.1 PFES "Ada" and helpful discussions with Martin Jaggi.  ... 
doi:10.1109/sds.2019.00-11 dblp:conf/sds2/TuggenerARLVWS19 fatcat:okiclde7nrb3xgyatmlqjh4use

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Ayan Chatterjee, Martin W. Gerdes, Santiago G. Martinez
2020 Sensors  
(d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?  ...  In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors.  ...  Acknowledgments: Thanks to "University of Agder, Department of Information and Communication Technology, Center for eHealth" for giving me the infrastructure to carry out this research task and my supervisors  ... 
doi:10.3390/s20092734 pmid:32403349 fatcat:enxjacosmzcxhd3rxbht5qldtq

Uncovering shared structures in multiclass classification

Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics  ...  We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.  ...  Acknowledgments We would like to thank Francis Bach for discussing his current related work with us and Alexandre d'Aspremont for helpful suggestions regarding optimization.  ... 
doi:10.1145/1273496.1273499 dblp:conf/icml/AmitFSU07 fatcat:v7riumikyva53otkcyzwdakttu

Artificial Intelligence and Its Applications 2014

Yudong Zhang, Saeed Balochian, Praveen Agarwal, Vishal Bhatnagar, Orwa Jaber Housheya
2016 Mathematical Problems in Engineering  
and gradient descent with momentum algorithm.  ...  with Beale, conjugate gradient with Fletcher Reeves updates, conjugate gradient with Polakribiere updates, one step secant, gradient descent, gradient descent with momentum and adaptive learning rate,  ...  Acknowledgments We would like to show our appreciation to all authors for their contributions and the reviewers for their effort providing  ... 
doi:10.1155/2016/3871575 fatcat:irj62qjsdzfu7h4fdslkgy5hny

Front Matter [chapter]

2015 Machine Learning in Python®  
Determining the Performance of a Gradient Boosting Classifier 298 Solving Multiclass Classification Problems with Python Ensemble Methods 302 Classifying Glass with Random Forests 302 Dealing  ...  and the Importance of Coded Variables with Gradient Boosting 282 Solving Binary Classification Problems with Python Ensemble Methods 284 Detecting Unexploded Mines with Python Random Forest 285  ... 
doi:10.1002/9781119183600.fmatter fatcat:f2hzkauhhfcrdgteqrlip32gee

Bilevel Programming for Hyperparameter Optimization and Meta-Learning [article]

Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimilano Pontil
2018 arXiv   pre-print
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning.  ...  We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective.  ...  The optimization of H is performed with gradient descent with momentum, with same initialization, step size and momentum factor for each run.  ... 
arXiv:1806.04910v2 fatcat:jwbxyr2obrb3rn27wflfcykvdi

Intrusion Detection using Machine Learning and Deep Learning

2019 International journal of recent technology and engineering  
ML and DL methods are compared with regard to their accuracy and detection potential to detect different types of intrusions.  ...  With the increase in usage of networking technology and the Internet, Intrusion detection becomes important and challenging security problem.  ...  The essential objective of LSTM is to accomplish vanishing gradient descent which is an enhancement calculation to discover artificial neural systems loads to stay away from long haul reliance issues  ... 
doi:10.35940/ijrte.d9999.118419 fatcat:nc5iw6tmbzh7pbtzuzcfhtxoxi

Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm

Kapilya Gangadharan, G. Rosline Nesa Kumari, D Dhanasekaran, K Malathi
2020 Indonesian Journal of Electrical Engineering and Computer Science  
The proposed system involves Enhanced Fractal Texture Feature Analysis and Machine Learning methodology for classification.  ...  followed in our proposed system is focused on Artificial Neural Network which uses Recursive Back Propagation Neural network to speed up the training process and the weights on ANN is optimized using Genetic  ...  Artificial deep neural network (ADNN) concept for disease classification has been proposed, which utilizes Recursive Backpropagation Neural Network in combination which uses gradient descent with more  ... 
doi:10.11591/ijeecs.v20.i3.pp1278-1288 fatcat:ji4h4ljpefdhjhmunafqzoxfge

Instruction-Matrix-Based Genetic Programming

Gang Li, Jin Feng Wang, Kin Hong Lee, Kwong-Sak Leung
2008 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Index Terms-Classification, condition matrix for rule learning (CMRL), genetic programming (GP), instruction-matrix-based genetic programming (IMGP), schema evolution.  ...  IMGP can also be used to evolve programs for classification problems.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1109/tsmcb.2008.922054 pmid:18632395 fatcat:refyzh5nx5efzgsms3abbrjmmi

Architecture Optimization Model for the Deep Neural Network

Kingsley Ukaoha, Efosa Igodan
2019 International Journal of Intelligent Computing and Information Sciences  
The genetic algorithm is used to select optimal network parameters for the Deep-NN.  ...  In this study, a novel approach that combines an evolutionary genetic algorithm and an optimization algorithm and a supervised deep neural network (Deep-NN) using alternative activation functions with  ...  and still achieve high accuracies for both the binary and multiclass classification problems.  ... 
doi:10.21608/ijicis.2019.96101 fatcat:lxnzf53b5rdlrkzwizfjh4ltnm

Comparison of linear, nonlinear, and feature selection methods for eeg signal classification

D. Garrett, D.A. Peterson, C.W. Anderson, M.H. Thaut
2003 IEEE transactions on neural systems and rehabilitation engineering  
An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.  ...  Index Terms-Brain-computer interface (BCI) , electroencephalogram (EEG), feature selection, genetic algorithms (GA), neural networks, pattern classification, support vector machines (SVM).  ...  [8] for a thorough development of the classification algorithms and Whitley [23] for an introduction to genetic algorithms.) Manuscript A.  ... 
doi:10.1109/tnsre.2003.814441 pmid:12899257 fatcat:d5ypsrxmrbe2jb7dfi4dyrhefi

Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods : Emperical Evidence From Turkey

Ebru KARAAHMETOĞLU, Süleyman ERSÖZ, Ahmet Kürşad TÜRKER, Volkan ATEŞ, Ali Firat İNAL
2021 Journal of Polytechnic  
Stochastic Gradient Descent The Stochastic Gradient Descent algorithm is a popular algorithm in machine learning and forms the basis for artificial neural networks [50] .  ...  Stochastic Gradient Descent Algorithm Gradient descent algorithm is presented as minimization of ( ) loss function by using gradient descent and graphically represented in Figure 4 [52] .  ... 
doi:10.2339/politeknik.985534 fatcat:67pqkybk25g2tns34bmkbfkrnm

An advanced Intrusion Detection System for IIoT Based on GA and Tree based Algorithms

Sydney Mambwe Kasongo
2021 IEEE Access  
The GA-RF generated 10 feature vectors for the binary classification scheme and 7 feature vectors for the multiclass classification procedure.  ...  In this research, we propose an IDS for IIoT that was implemented using the Genetic Algorithm (GA) for feature selection, and the Random Forest (RF) model was employed in the GA fitness function.  ...  The optimization algorithm applied to the LSTM is Stochastic Gradient Descent (SGD).  ... 
doi:10.1109/access.2021.3104113 fatcat:de35vkgrkzclfputdgz4qaz2d4
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