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The perceptron is a simplified model of a biological neuron. While the complexity of biological neuron models is often required to fully understand neural behavior, research suggests a perceptron-like linear model can produce some behavior seen in real neurons. [30]
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 ...
Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features.
Word2vec was created, patented, [7] and published in 2013 by a team of researchers led by Mikolov at Google over two papers. [1] [2] The original paper was rejected by reviewers for ICLR conference 2013. It also took months for the code to be approved for open-sourcing. [8] Other researchers helped analyse and explain the algorithm. [4]
Website with academic papers about security topics. This data is not pre-processed Papers per category, papers archive by date. [379] Trendmicro Website with research, news, and perspectives bout security topics. This data is not pre-processed Reviewed list of Trendmicro research, news, and perspectives. [380] The Hacker News
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
Robert Schapire answered the question in the affirmative in a paper published in 1990. [5] This has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting. [6] Initially, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. [3]
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.