Comparative Study of Different Orange Data Mining Tool-Based AI Techniques in Image Classification

[1]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[2]  Blaz Zupan,et al.  Orange: Data Mining Fruitful and Fun - A Historical Perspective , 2013, Informatica.

[3]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[4]  Gongzhu Hu,et al.  Predicting the characteristics of people living in the South USA using logistic regression and decision tree , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[5]  Devashree Vaishnav,et al.  Comparison of Machine Learning Algorithms and Fruit Classification using Orange Data Mining Tool , 2018, 2018 3rd International Conference on Inventive Computation Technologies (ICICT).

[6]  Subhashree Mohapatra,et al.  Artificial Intelligence for Smart Healthcare Management: Brief Study , 2020 .

[7]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[8]  Devavrat Shah,et al.  Explaining the Success of Nearest Neighbor Methods in Prediction , 2018, Found. Trends Mach. Learn..

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Han Liu,et al.  Decision tree learning based feature evaluation and selection for image classification , 2017, 2017 International Conference on Machine Learning and Cybernetics (ICMLC).

[11]  R Muthukrishnan,et al.  LASSO: A feature selection technique in predictive modeling for machine learning , 2016, 2016 IEEE International Conference on Advances in Computer Applications (ICACA).

[12]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Sander M. Bohte,et al.  Editorial: Artificial Neural Networks as Models of Neural Information Processing , 2017, Front. Comput. Neurosci..