A novel intrusion detection framework based on particle swarm optimization (PSO) was proposed by Jiang Tian and Gu [70] which had combined the idea of unsupervised learning method and the supervised strategy.
Fine-tuning treats all layers of SAE as a single neural network model; in each iteration, it improves all the parameters from the last iteration, and it can improve the result of layer-wise training by a supervised strategy.
Through a semi-supervised strategy, the new dictionary learning method, termed JSM-DKSVD, utilises all spectrums over the local neighbourhoods of labelled training pixels for discriminative dictionary learning.
Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data.
In the next section we look at a related semi-supervised strategy of predicting novel functional modules within the unclassified ORFs.
In the current study, we applied a global and non-supervised strategy to explore potential key signaling events conferring RT responsiveness or resistance in NSCLC cells.
Chen et al. [12] have proposed a hybrid approach that mixes extraction of key phrase, textual, financial data to ascertain the vicious of phishing attack using supervised classification strategies.
Due to some characteristics of real document collections, e.g., class imbalance and feature sparseness, NB solutions do not present competitive effectiveness in some ADC tasks when compared to other supervised learning strategies, e.g., SVMs.
Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation.
