2016/01/01 by Luca Masera, Masera, Luca, Enrico Blanzieri +2
Computer Science · Mathematics · #Blind Source Separation Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1609.04321
openalex publication_date 2016/01/01 · arxiv created 2016/09/14 · arxiv updated 2016/09/15 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin classifier built on randomly-selected pairs of samples. The locality in VSC is achieved by multiplying the value of the feature by a confidence measure that can be characterized in terms of the Chebichev inequality. The output of the layer is then fed in a output layer of neurons. The weights of the output layer are then determined by a regularized pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of binary classification is carried out. Results on 22 benchmark datasets with fixed parameters show that VSC is competitive with the Multi Layer Perceptron (MLP) and outperforms the other competitors. An exploration of the parameter space shows VSC can outperform MLP.