Traffic Sign Classification with Keras and Deep Learning ...

Figure 3: The German Traffic Sign Recognition Benchmark (GTSRB) dataset is an example of an unbalanced dataset. We will account for this when training our traffic sign classifier with Keras and deep learning. (image source)The top class (Speed limit 50km/h) has over 2,000 examples while the least represented class (Speed limit 20km/h) has under 200 examples — that's an order of …

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Each blue dashed line represents one NP classifier, with horizontal coordinate α and vertical coordinates 1 − β U (lower) and 1 − β L (upper). Right-continuous and left-continuous step functions are used to interpolate points on the upper and lower ends, respectively. (B) Use of NP-ROC bands to compare the two LDA methods in Simulation 2.

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This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.

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Background . Diffuse large B cell lymphoma (DLBCL) is a life-threatening malignant tumor characterized by heterogeneous clinical, phenotypic, and molecular manifestations. Given the association between immunity and tumors, identifying a suitable immune biomarker could improve DLBCL diagnosis. Methods . We systematically searched for DLBCL gene expression microarray datasets from the GEO …

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Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.

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Ensemble Methods . The idea of combining different classifiers is often called ensemble learning. If the classifiers we are combining are all decent but different (i.e., make different, uncorrelated errors) than simple voting improves over each individual classifier since the errors cancel out.

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