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Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous ...
Feature engineering is a preprocessing step in supervised machine learning and statistical modeling [1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features.
scikit-learn. 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 ...
Machine learningand data mining. In machine learning, support vector machines ( SVMs, also support vector networks [1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.
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Functionality VTK consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python. The toolkit is created and supported by the Kitware team. VTK supports a various visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing ...
Random sample consensus ( RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. [1] It is a non-deterministic algorithm in ...
The scale-invariant feature transform ( SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. [1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving .