Parameter adjustment refers to the act of adjusting parameters in order to obtain better results.

Numerical attributes are a type of attribute that quantitatively describes data, meaning that the data is a measurable quantity.

General artificial intelligence refers to an intelligent entity with the same or superior capabilities as humans. It is also called strong artificial intelligence, which can display all intelligent behaviors of normal humans. In order to distinguish it from the AI term of traditional artificial intelligence or mainstream artificial intelligence, a general prefix is added.

Attribute space: The space formed by attributes is also called "sample space" or "input space". Feature space: The attribute space formed by the selected attributes after excluding linear correlation and attributes that are not beneficial to model construction is called feature space. Related concepts Data set […]

The naive Bayes classifier uses the "attribute conditional independence assumption": for known categories, it is assumed that all attributes are independent of each other. Improved naive Bayes: In order to prevent the information carried by other attributes from being "erased" by attribute values that have never appeared in the training set, "smoothing" is usually performed when estimating probability values, and the "Laplace correction" is often used; for […]

Generative adversarial networks are an unsupervised learning method that is implemented by letting two neural networks compete with each other. This method was proposed by Ian Goodfellow in 2014. Generative adversarial networks include a generative network and a discriminative network. The generative network takes random samples in the latent space as input, and the output needs to imitate the training […]

In machine learning, generative models can be used to directly model data or to establish conditional probability distributions between variables. Conditional probability distributions can be based on generative models of Bayes’ theorem. Generative models are suitable for unsupervised tasks such as classification and clustering. Typical generative models include the following: Gaussian mixture models and other mixed […]

Graph theory is a branch of combinatorial mathematics. Its main research object is graph. Graph here refers to a figure composed of several given vertices and edges connecting two vertices. It is often used to describe special relationships between things, where vertices represent things and edges represent the connection between them. Graph theory originated from the Königsberg Seven Bridges Problem, which was solved by Euler in 1736. […]

The gradient explosion problem usually occurs in deep networks and when the weight initialization value is too large. It usually becomes more obvious as the number of network layers increases. By taking the derivative of the activation function, if the result is greater than 1, then as the number of layers increases, the final gradient update will increase exponentially, i.e., a gradient explosion occurs; if the result is less than 1, then the number of layers […]

Eigendecomposition is a method of representing the product of matrices by decomposing them into eigenvalues and eigenvectors. However, only diagonal matrices can be eigendecomposed. Matrix multiplication corresponds to a transformation, which is to transform any vector into a new vector of another direction and length. In this process, the original vector will be rotated and stretched.

An algorithm is an efficient way of representing a finite-length list. In mathematics and computer science, an algorithm can be considered as any well-defined sequence of specific computational steps.

Harmonic mean is a method of calculating averages, which can be divided into simple and weighted forms. The weighted harmonic mean is a variation of the weighted arithmetic mean. In most cases, we only know the sum of the values of a certain sign in each group, m, but lack information about the number of units in the population, so we cannot directly use the weighted arithmetic mean method to calculate. Instead, […]

Trial and error is a method of solving problems by repeated attempts.

Slack variables are auxiliary variables added when applying the soft interval method for classification. They are introduced to address the impact of outliers on classification.

Stochastic gradient descent (SGD) is an iterative solution approach of the gradient descent algorithm.

A surrogate function is a function that is used when the target function cannot be used or performs poorly.

The loss function is a metric used to measure the quality of a prediction model. It reflects the gap between the model's predicted value and the true value. It is the core part of the empirical risk function and also a component of the structural risk function. Common loss functions Log loss function Square loss function Exponential loss function Hinge loss function

Feature selection is the process of selecting feature subsets. It is usually used to build models. Its advantages are as follows: Simplify the model; Shorten the training time; Improve versatility and reduce overfitting. Feature selection algorithm can be regarded as a combination of search technology and evaluation index. The former provides candidate new feature subsets, and the latter is used to select different feature subsets.

The objective function refers to the form of the goal pursued expressed by the design variables and is a function of the design variables.

Reinforcement learning (RL) is an important branch of machine learning and a product of the intersection of multiple disciplines and fields. Its essence is to solve the decision-making problem, that is, to make decisions automatically and continuously.

The scoring function is the type of "score" available for the selected model. For example, the predicted value of the target, the probability of a predicted value, or the probability of a selected target value.

Singular value decomposition (SVD) is an important matrix decomposition method. The basis of eigenvector decomposition of symmetric arrays is spectral analysis, and singular value decomposition is the generalization of spectral analysis theory to arbitrary matrices.

Soft voting is also called weighted average probability voting. It is a voting method that uses the output class probability for classification. By inputting weights, the weighted average of the class probability of each class is obtained, and the class with the larger value will be selected.

Spectral clustering (SC) is a clustering method based on graph theory. It divides a weighted undirected graph into two or more optimal subgraphs, making the subgraphs as similar as possible and the distances between subgraphs as far as possible, so as to achieve the common clustering purpose.

Parameter adjustment refers to the act of adjusting parameters in order to obtain better results.

Numerical attributes are a type of attribute that quantitatively describes data, meaning that the data is a measurable quantity.

General artificial intelligence refers to an intelligent entity with the same or superior capabilities as humans. It is also called strong artificial intelligence, which can display all intelligent behaviors of normal humans. In order to distinguish it from the AI term of traditional artificial intelligence or mainstream artificial intelligence, a general prefix is added.

Attribute space: The space formed by attributes is also called "sample space" or "input space". Feature space: The attribute space formed by the selected attributes after excluding linear correlation and attributes that are not beneficial to model construction is called feature space. Related concepts Data set […]

The naive Bayes classifier uses the "attribute conditional independence assumption": for known categories, it is assumed that all attributes are independent of each other. Improved naive Bayes: In order to prevent the information carried by other attributes from being "erased" by attribute values that have never appeared in the training set, "smoothing" is usually performed when estimating probability values, and the "Laplace correction" is often used; for […]

Generative adversarial networks are an unsupervised learning method that is implemented by letting two neural networks compete with each other. This method was proposed by Ian Goodfellow in 2014. Generative adversarial networks include a generative network and a discriminative network. The generative network takes random samples in the latent space as input, and the output needs to imitate the training […]

In machine learning, generative models can be used to directly model data or to establish conditional probability distributions between variables. Conditional probability distributions can be based on generative models of Bayes’ theorem. Generative models are suitable for unsupervised tasks such as classification and clustering. Typical generative models include the following: Gaussian mixture models and other mixed […]

Graph theory is a branch of combinatorial mathematics. Its main research object is graph. Graph here refers to a figure composed of several given vertices and edges connecting two vertices. It is often used to describe special relationships between things, where vertices represent things and edges represent the connection between them. Graph theory originated from the Königsberg Seven Bridges Problem, which was solved by Euler in 1736. […]

The gradient explosion problem usually occurs in deep networks and when the weight initialization value is too large. It usually becomes more obvious as the number of network layers increases. By taking the derivative of the activation function, if the result is greater than 1, then as the number of layers increases, the final gradient update will increase exponentially, i.e., a gradient explosion occurs; if the result is less than 1, then the number of layers […]

Eigendecomposition is a method of representing the product of matrices by decomposing them into eigenvalues and eigenvectors. However, only diagonal matrices can be eigendecomposed. Matrix multiplication corresponds to a transformation, which is to transform any vector into a new vector of another direction and length. In this process, the original vector will be rotated and stretched.

An algorithm is an efficient way of representing a finite-length list. In mathematics and computer science, an algorithm can be considered as any well-defined sequence of specific computational steps.

Harmonic mean is a method of calculating averages, which can be divided into simple and weighted forms. The weighted harmonic mean is a variation of the weighted arithmetic mean. In most cases, we only know the sum of the values of a certain sign in each group, m, but lack information about the number of units in the population, so we cannot directly use the weighted arithmetic mean method to calculate. Instead, […]

Trial and error is a method of solving problems by repeated attempts.

Slack variables are auxiliary variables added when applying the soft interval method for classification. They are introduced to address the impact of outliers on classification.

Stochastic gradient descent (SGD) is an iterative solution approach of the gradient descent algorithm.

A surrogate function is a function that is used when the target function cannot be used or performs poorly.

The loss function is a metric used to measure the quality of a prediction model. It reflects the gap between the model's predicted value and the true value. It is the core part of the empirical risk function and also a component of the structural risk function. Common loss functions Log loss function Square loss function Exponential loss function Hinge loss function

Feature selection is the process of selecting feature subsets. It is usually used to build models. Its advantages are as follows: Simplify the model; Shorten the training time; Improve versatility and reduce overfitting. Feature selection algorithm can be regarded as a combination of search technology and evaluation index. The former provides candidate new feature subsets, and the latter is used to select different feature subsets.

The objective function refers to the form of the goal pursued expressed by the design variables and is a function of the design variables.

Reinforcement learning (RL) is an important branch of machine learning and a product of the intersection of multiple disciplines and fields. Its essence is to solve the decision-making problem, that is, to make decisions automatically and continuously.

The scoring function is the type of "score" available for the selected model. For example, the predicted value of the target, the probability of a predicted value, or the probability of a selected target value.

Singular value decomposition (SVD) is an important matrix decomposition method. The basis of eigenvector decomposition of symmetric arrays is spectral analysis, and singular value decomposition is the generalization of spectral analysis theory to arbitrary matrices.

Soft voting is also called weighted average probability voting. It is a voting method that uses the output class probability for classification. By inputting weights, the weighted average of the class probability of each class is obtained, and the class with the larger value will be selected.

Spectral clustering (SC) is a clustering method based on graph theory. It divides a weighted undirected graph into two or more optimal subgraphs, making the subgraphs as similar as possible and the distances between subgraphs as far as possible, so as to achieve the common clustering purpose.