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Unsupervised Learning (PCA) on Vehicle dataset

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pca logistic-regression support-vector-machine naive-bayes-classification unsupervised-machine-learning machine-learning supervised-machine-learning principal-component-analysis principal-components machine-learning-algorithms machinelearning unsupervised-learning-algorithms unsupervised supervised-learning supervised-learning-algorithms naive-bayes-classifier naive-bayes-algorithm

classifying-silhouettes-of-vehicles's Introduction

Classifying-silhouettes-of-vehicles

Problem statement

The objective is to classify a given silhouette as one of three types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.

Dataset Description

The features were extracted from the silhouettes by the HIPS (Hierarchical Image Processing System) extension BINATTS, which extracts a combination of scale independent features utilising both classical moments based measures such as scaled variance, skewness and kurtosis about the major/minor axes and heuristic measures such as hollows, circularity, rectangularity and compactness.

Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.

References

UCI: https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes)

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