In this paper, min鈥搈ax normalization-based data transformation method is used to protect the sensitive information in a dataset as well as to achieve good data mining results. The proposed method is applied on the adult dataset and the accuracy of the results is compared with Nave Bayes ...
support vector machine (SVM), non-negative matrix factorization, and latent Dirichlet allocation to identify spam [40], while Almeida et al. suggested text normalization [41]. Fattahi and Mejri applied natural language processing (NLP)
For better comparison, max-min normalization is utilized to unify different datasets to the same reference system. Figure 14e shows that OPTTS and OLTS perform relatively stable on all datasets, while OPW, D-P, and MRPA show a large fluctuation. Visualization of Compression Result. The ...
cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow....
In recent years, privacy preserving data mining is an important one because wide availability of data is there. It is used for protecting the privacy of the critical and sensitive data and obtains more accurate results of data mining. The random noise is added to th...
cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow....