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Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less(More)
The gradual Cl replacement reactions of NN (1–3) or NO spirocyclic monoferrocenyl cyclotriphosphazenes (4 and 5) with the potassium salt of 4-hydroxy-3-methoxybenzaldehyde (potassium vanillinate) resulted in the mono (1a–5a), geminal (gem-1b–5b), non-geminal (cis-5b and trans -1b–4b), tri (1c, 3c–5c) and tetra-vanillinato-substituted phosphazenes (1d–5d).(More)
The feature selection approach provides improved prediction and minimizes the computation time. Due to the higher numbers of features the understanding of the data in pattern recognition becomes difficult sometimes. That's why researchers have used different feature selection techniques with the single classifiers in their intrusion detection system to(More)
Utilization of the data mining techniques in intrusion detection systems is common for the classification of the network events as either normal events or attack events. Naïve Bayes (NB) method is a simple, efficient and popular data mining method that is built on conditional independence of attributes assumption. Hidden Naïve Bayes (HNB) is(More)
Overview We've organized our presentation into three stages: 1. A more detailed coverage of the building blocks of CNNs 2. Attempts to explain how and why Residual Networks work 3. Survey extensions to ResNets and other notable architectures Topics covered: ● Alternative activation functions ● Relationship between fully connected layers and convolutional(More)
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