An optimal start node for beginning your machine learning journey
↖️ KMeans↖️ K-Nearest Neighbors(KNN)↖️ Linear Discriminant Analysis(LDA)↖️ Logistic Regression↖️ Naïve Bayes↖️ Principal Component Analysis(PCA)↖️ StochasticGradientDescent(SGD)↖️ Perceptron↖️ Regression
SVMs multi-class loss feedback based discriminative dictionary learning for image classification
SMLFDL integrates dictionary learning and support vector machines training into a unified learning framework by looping the designed multi-class loss term, which is inspired by the feedback mechanism in cybernetics.
analysis has been done on scene-15 dataset.
Feature vectors has been prepared by four-level spatial pyramid
, dense DAISY
feature description followed by PCA.
As article proposed SMLFDL are faster in predictions and converge in lower epochs.
code for features will be added soon.