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Pairs of images and tweets 100,000 Text and Images Cross-media retrieval 2017 [45] [46] Y. Hu, et al. Sentiment140 Tweet data from 2009 including original text, time stamp, user and sentiment. Classified using distant supervision from presence of emoticon in tweet. 1,578,627 Tweets, comma, separated values Sentiment analysis 2009 [47] [48] A ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
In mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed to become negative.
The R package "dbscan" includes a C++ implementation of OPTICS (with both traditional dbscan-like and ξ cluster extraction) using a k-d tree for index acceleration for Euclidean distance only. Python implementations of OPTICS are available in the PyClustering library and in scikit-learn. HDBSCAN* is available in the hdbscan library.
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.
NMF can be seen as a two-layer directed graphical model with one layer of observed random variables and one layer of hidden random variables. [47] NMF extends beyond matrices to tensors of arbitrary order. [48] [49] [50] This extension may be viewed as a non-negative counterpart to, e.g., the PARAFAC model.
In mixture of softmaxes, the model outputs multiple vectors ,, …,,, and predict the next word as = (,), where is a probability distribution by a linear-softmax operation on the activations of the hidden neurons within the model. The original paper demonstrated its effectiveness for recurrent neural networks. This was later found to work for ...
The ImageNet training set contained 1.2 million images. The model was trained for 90 epochs over a period of five to six days using two Nvidia GTX 580 GPUs (3GB each). [1] These GPUs have a theoretical performance of 1.581 TFLOPS in float32 and were priced at US$500 upon release. [3] Each forward pass of AlexNet required approximately 4 GFLOPs. [4]