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Margin maximization with feed-forward neural networks: a comparative study with SVM and AdaBoost

Enrique Romero, Lluı́s Màrquez, Xavier Carreras
2004 Neurocomputing  
Feed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very di erent starting points of view.  ...  Several empirical comparisons among this new model, SVM, and AdaBoost have been made in order to study the agreement between the predictions made by the respective classiÿers.  ...  Acknowledgements The authors thank the anonymous reviewers for their valuable comments and suggestions in order to prepare the ÿnal version of the paper.  ... 
doi:10.1016/j.neucom.2003.10.011 fatcat:umszhcqwz5dzzmutcnxbd3gvte

Margin-Based Feed-Forward Neural Network Classifiers [article]

Han Xiao, Xiaoyan Zhu
2015 arXiv   pre-print
Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture.  ...  In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network  ...  feed-forward neural networks.  ... 
arXiv:1506.03626v1 fatcat:bp2bojj3ffbofd2gltfek7oqca

Margin-Based Feed-Forward Neural Network Classifiers

Han Xiao, Xiaoyan Zhu
2015 Zenodo  
Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture.  ...  In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network  ...  Principle into Feed-Forward neural network and Margin-Based Principle works.  ... 
doi:10.5281/zenodo.1106372 fatcat:jlgdy37pg5gg7e5grlvpltyqaa

Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

P Kalaivani, St.Joseph's College of Engineering, Chennai
2020 Indian Journal of Science and Technology  
Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning.  ...  Objectives: The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature  ...  The neural network based measure was proposed to assist companies in quickly and effectively finding bad comments. Performance analysis had been performed for "feed forward pattern network".  ... 
doi:10.17485/ijst/v13i28.927 fatcat:bjiibm5exncwlgljyocgtkb5cy

Comparing Performances of Cuckoo Search Based Neural Networks [chapter]

Nazri Mohd Nawi, Abdullah Khan, M. Z. Rehman, Tutut Herawan, Mustafa Mat Deris
2014 Advances in Intelligent Systems and Computing  
These limitations are mostly originated from the low reliability and also high generalization error of a single neural network classifier which may be faced in real applications.  ...  This work employs potential and distinct features of artificial neural networks, in particular the feedforward multilayer structure, to achieve fault diagnosis in chemical plants.  ...  The networks used here are single hidden layer feed-forward neural networks. For each case, the number of output neurons is fixed.  ... 
doi:10.1007/978-3-319-07692-8_16 fatcat:wovuonitrjbqtkgxxwsf3lfmqu

Artificial intelligence enabled software-defined networking: a comprehensive overview

Majd Latah, Levent Toker
2019 IET Networks  
Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability.  ...  SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network.  ...  In this paper, the term deep neural networks (DNN) refers to deep feed-forward multilayer networks or multilayer perceptrons (MLPs).  ... 
doi:10.1049/iet-net.2018.5082 fatcat:celiaiit7jhrfnufpoltuecf5y

Application of Deep Belief Networks for Natural Language Understanding

Ruhi Sarikaya, Geoffrey E. Hinton, Anoop Deoras
2014 IEEE/ACM Transactions on Audio Speech and Language Processing  
CD allows DBNs to learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation  ...  We compare a DBN-initialized neural network to three widely used text classification algorithms: support vector machines (SVM), boosting and maximum entropy (MaxEnt).  ...  DBNs use unsupervised learning to discover multiple layers of features that are then used in a feed-forward neural network and fine-tuned to optimize discrimination.  ... 
doi:10.1109/taslp.2014.2303296 fatcat:abcie3caqvgu7lspzbfv2m6xfq

Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver's Distraction Detection

Tahir Abbas, Syed Farooq Ali, Mazin Abed Mohammed, Aadil Zia Khan, Mazhar Javed Awan, Arnab Majumdar, Orawit Thinnukool
2022 Applied Sciences  
The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver.  ...  The study also compares ReSVM with its variants on the aforementioned datasets.  ...  The objective of the SVM algorithm is to find a maximum separating margin between the hyperplane and the data points. For maximizing the margins, hinge loss is used as a loss function.  ... 
doi:10.3390/app12136626 fatcat:3uk4pv3trnebfdjz575abxldbu

Feed Distillation Using AdaBoost and Topic Maps

Wai-Lung Lee, Andreas Lommatzsch, Christian Scheel
2007 Text Retrieval Conference  
To perform the run various classifiers are combined, which analyze title-, content-and splog-specific features to predict the relevance of a feed related to a topic, based on the idea of AdaBoost.  ...  Project' and Yahoo Directory.  ...  In the research literature, many comparisons exist between SVM and AdaBoost or AdaBoost and Feed-forward Neural Network (FNN).  ... 
dblp:conf/trec/LeeLS07 fatcat:3ej74pcbazcotpr53iqc7zc66a

Boosting Methods for Protein Fold Recognition: An Empirical Comparison

Yazhene Krishnaraj, Chandan K. Reddy
2008 2008 IEEE International Conference on Bioinformatics and Biomedicine  
(KNN) and Neural Networks (NN).  ...  Prediction accuracy is measured on a dataset with proteins from 27 most populated folds from the SCOP database, and is compared with results from other literature using SVM, KNN and NN algorithms on the  ...  Ding and Dubchak [2] used a three-layer feed-forward NNs in their experiments.  ... 
doi:10.1109/bibm.2008.83 dblp:conf/bibm/KrishnarajR08 fatcat:tdsku2sefzabncrysmjcwvryki

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

Sheraz Naseer, Rao Faizan Ali, P.D.D Dominic, Yasir Saleem
2020 Symmetry  
A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered  ...  In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems.  ...  Acknowledgments: We are thankful for the help and guidance provided by P.D.D. Dominic and Yasir Saleem; without their advice, it would have been impossible to achieve goals of this research.  ... 
doi:10.3390/sym12111882 fatcat:gs2oqpz525hifldtegp3ecp5oe

Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

Ahmad LG, Eshlaghy AT
2013 Journal of Health & Medical Informatics  
., Decision Tree (C4.5), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to develop the predictive models.  ...  The SVM classification model predicts breast cancer recurrence with least error rate and highest accuracy. The predicted accuracy of the DT model is the lowest of all.  ...  Neural networks are a large number of interconnected nodes The back-propagation algorithm can be employed effectively to train neural networks; it is widely recognized for applications to layered feed-forward  ... 
doi:10.4172/2157-7420.1000124 fatcat:xhzrbnrqizgz7f6lsayyts34y4

Learning From Examples in the Small Sample Case: Face Expression Recognition

G. Guo, C.R. Dyer
2005 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost.  ...  To study this issue, the task of face expression recognition with a small number of training images of each expression is considered.  ...  Mangasarian and S. Wright for their help on the linear programming technique, and M. Lyons for providing the face expression database.  ... 
doi:10.1109/tsmcb.2005.846658 pmid:15971916 fatcat:tykbzdbz2zg47om4rjihkoh66u

Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks [article]

Nurendra Choudhary, Rajat Singh, Ishita Bindlish, Manish Shrivastava
2018 arXiv   pre-print
SNASA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function, based on a similarity metric.  ...  approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without sh  ...  Adaboost based Convolution Neural Networks (Ada-CNN) [10] uses CNN classifiers with different filter sizes. Adaboost arrives at a weighted combination of the classifiers.  ... 
arXiv:1804.00805v1 fatcat:wvtdzazgsvc5veqrjslowf3jyy

Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study

Luís Chaves, Gonçalo Marques
2021 Applied Sciences  
Naive Bayes, Neural Network, AdaBoost, k-Nearest Neighbors, Random Forest and Support Vector Machine methods have been tested.  ...  This work proposes a comparative study of data mining techniques for early diagnosis of diabetes. We use a publicly accessible data set containing 520 instances, each with 17 attributes.  ...  Sasar conducted a study using Orange Tool to analyse a diabetes data set. Authors implemented and compared Random Forest, Feed-Forward Artificial Neural Networks, kNN, SVM and Decision Tree.  ... 
doi:10.3390/app11052218 doaj:d3bad609bf8549268b4d2c74ebc50c49 fatcat:udxbvgweyrh4hmaujpwmls6cee
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