Image processing research papers 2011

Two years ago, we introduced the first high-level machine learning functions of the Wolfram Language , Classify and Predict . Since then, we have been creating a set of automatic machine learning functionalities ( ClusterClassify , DimensionReduction , etc.). Today, I am happy to present a new function called FeatureExtraction that deals with another important machine learning task: extracting features from data. Unlike Classify and Predict , which follow the supervised learning paradigm, FeatureExtraction belongs to the unsupervised learning paradigm, meaning that the data to learn from is given as a set of unlabeled examples (. without an input -> output relation). The main goal of FeatureExtraction is to transform these examples into numeric vectors (often called feature vectors). For example, let’s apply FeatureExtraction to a simple dataset:

Image processing research papers 2011

image processing research papers 2011

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image processing research papers 2011image processing research papers 2011image processing research papers 2011image processing research papers 2011