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Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. [1] Application domains include cluster analysis in computer vision and image processing .
For proper loss functions, the loss margin can be defined as = ′ ″ and shown to be directly related to the regularization properties of the classifier. [9] Specifically a loss function of larger margin increases regularization and produces better estimates of the posterior probability.
Typically, the number of training epochs or training set size is plotted on the x-axis, and the value of the loss function (and possibly some other metric such as the cross-validation score) on the y-axis.
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A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
The loss function in contrastive learning is used to minimize the distance between positive sample pairs, while maximizing the distance between negative sample pairs. [ 9 ] An early example uses a pair of 1-dimensional convolutional neural networks to process a pair of images and maximize their agreement.
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
Computer stereo vision takes two or more images with known relative camera positions that show an object from different viewpoints. For each pixel it then determines the corresponding scene point's depth (i.e. distance from the camera) by first finding matching pixels (i.e. pixels showing the same scene point) in the other image(s) and then ...