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- Hermitian Matrix Description: A Hermitian matrix is a square matrix that is equal to its own conjugate transpose, meaning that for a matrix A, it holds that A =(...) Read more
- Hessian Matrix Description: The Hessian matrix is a fundamental tool in the field of mathematical optimization. It is a square matrix that contains all the(...) Read more
- Heaviside Step Function Description: The Heaviside step function, commonly denoted as H(x), is a discontinuous mathematical function that takes the value of zero for(...) Read more
- Homotopy Description: Homotopy is a fundamental concept in topology that deals with the properties of topological spaces that are preserved under(...) Read more
- Hyperbola Description: The hyperbola is a type of smooth curve found in a plane, defined as the set of all points where the absolute difference of the(...) Read more
- Holomorphic Function Description: A holomorphic function is a complex function that is differentiable at every point in its domain, implying that it is not only(...) Read more
- Heteroscedasticity Test Description: Heteroscedasticity test is a fundamental statistical tool in regression analysis used to assess whether the variance of errors in a(...) Read more
- Harmonic Polynomial Description: A harmonic polynomial is a mathematical function that can be expressed as a linear combination of harmonic functions, which are(...) Read more
- HDBSCAN Description: HDBSCAN is a clustering algorithm that extends DBSCAN by turning it into a hierarchical clustering algorithm. Its name comes from(...) Read more
- Huber Loss Description: Huber's loss is a loss function used in robust regression, designed to be less sensitive to outliers compared to traditional(...) Read more
- Heteroscedastic Regression Description: Heteroscedastic regression is a regression analysis where the variance of errors varies across observations. Unlike homoscedastic(...) Read more
- Hinge Loss Function Description: The hinge loss function is a loss function used to train classifiers, especially support vector machines (SVM). Its main goal is to(...) Read more
- High Bias Description: High bias refers to a model that makes strong assumptions about the data, leading to underfitting. This phenomenon occurs when a(...) Read more
- High Variance Description: High variance refers to a machine learning model that is overly complex and thus able to capture not only the underlying trends in(...) Read more
- Heterogeneous Feature Selection Description: Heterogeneous feature selection involves selecting features from different types of data sources, allowing for the integration of(...) Read more
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