Feature are extracted from the CT images using Coif let wavelets; 2. The features extracted are classified using Support Vector Machine; 3. The parameters of the SVM are optimized using Particle Swarm Optimization (PSO), and modified PSO with a genetic algorithm...
In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings ...
Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have... Godwin,Caruana,and,... - 《Neurocomputing》 被引量: 39发表: 2013年 About the non-convex optimization problem induced by non-positive se...
Jiang et al.35preformed optimization for classification of high-density sEMG signals (256 channels) by feature extraction and data augmentation methods. 50 known temporal-spectral-spatial domain features, including a new introduced feature denoted by the spatial synchronization (SS) feature, which measur...
rock.The support design and techniques of repairing and strengthening for haulage roadways are proposed according to the table.The field monitoring result shows that it can control the deformation of roadway effectively.The table will find promising engineering application in the support design and ...
et al. Improvement of power quality parameters using modulated-unified power quality conditioner and switched-inductor boost converter by the optimization techniques for a hybrid AC/DC microgrid. Sci. Rep. 12, 21675. https://doi.org/10.1038/s41598-022-26001-8 (2022). Article ADS PubMed Pub...
We show that this problem can be addressed by a fully automated approach, leveraging recent innovations in Bayesian optimization. Specifically, we consider a wide range of feature selection techniques (combining 3 search and 8 evaluator methods) and all classification approaches implemented in WEKA's ...
Compared to older, calculus-based numerical optimization techniques such as gradient descent and L-BFGS, based on my experience, training using MSO tends to produce better ML models, but MSO is almost always an order of magnitude slower. Dr. James McCaffrey works for Microsoft Research in Redmond...
In this paper, we compare the performances of Singular Value Decomposition (SVD) and K-means clustering techniques in the optimization of fuzzy outputs towards the classification of epilepsy risk levels from EEG (Electroencephalogram) signals. The fuzzy techniques are applied as basic classifier to cla...
Early classification of time series using multi-objective optimization techniques Information Sciences (2019) MahardhikaPratamaet al. Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams Information Sciences ...