Wiki

Resampling refers to extracting repeated samples from the original data sample. This is a non-parametric method of statistical inference. That is, resampling does not use a general distribution to approximate the value of probability p.

Residual mapping is the corresponding relationship based on which the residual network is constructed. Its common form is H ( x ) = F ( x ) + x , where F ( x ) is the residual function.

Pooling, also called spatial pooling, is a method used to extract features in convolutional neural networks.

Computer vision is a science that studies how to make machines "see". Specifically, it refers to the use of cameras and computers to replace human eyes to identify, track and measure targets, and use computers to process images into images that are more suitable for human observation or transmission to instruments for detection. Definition Computer vision is the use of computers and related […]

Computational linguistics is a discipline that uses mathematical models to analyze and process natural languages, and uses programs on computers to implement the analysis and processing process, thereby achieving the goal of using machines to simulate some or all of a person's language abilities. Basic content Computational linguistics can be divided into the following three categories according to the nature and complexity of its work: Automatic editing: […]

Eigen decomposition is a method of decomposing a matrix into a product of matrices represented by eigenvalues and eigenvectors. However, only diagonalizable matrices can be subjected to eigendecomposition. The eigenvalue can be regarded as the scaling ratio of the length of the eigenvector under linear changes. If the eigenvalue is positive, it means that $latex v $ has been subjected to linear transformation […]

Definition of Backpropagation Backpropagation, short for "error backpropagation", is a common method used in conjunction with optimization methods to train artificial neural networks. This method calculates the gradient of the loss function for all weights in the network. This gradient is fed back to the optimization method to update the weights to minimize the loss function. […]

Backpropagation through time is a backpropagation algorithm applied to recurrent neural networks (RNNs). BPTT can be viewed as a standard backpropagation algorithm applied to RNNs, where each time step represents a computational layer and its parameters are shared across computational layers. Since RNNs use the same algorithm at all time steps […]

The general base learner can be composed of Logistic regression, decision tree, SVM, neural network, Bayesian classifier, K-nearest neighbor, etc. If the individual learners are generated from the same learning algorithm from the training data, it can be called a homogeneous ensemble, and the individual learners in this case are also called base learners; the ensemble can also contain different […]

Definition Assume that x is a continuous random variable whose distribution depends on the class state, expressed in the form of p(x|ω). This is the "class conditional probability" function, that is, the probability function of x when the class state is ω. Class conditional probability function $latex P\left(X | w_{i}\ri […]

CART is a learning method for the conditional probability distribution of the output random variable Y given the input random variable X. Definition CART assumes that the decision tree is a binary tree, the internal node features have values ​​of "yes" and "no", the left branch is the branch with the value of "yes", and the right branch is the branch with the value of "no". This […]

Class imbalance is a binary classification problem in which the labels of the two classes have a large difference in frequency. For example, in a disease dataset, 0.0001 of the samples have positive class labels and 0.9999 of the samples have negative class labels, which is a class imbalance problem; but in a […]

Closed form refers to some strict formulas in which any independent variable can be given to find the dependent variable, that is, the solution to the problem. This is a form of solution that includes basic functions such as fractions, trigonometric functions, exponentials, logarithms, and even infinite series. The method used to find the relevant solution is also called analytical method, which is a common calculus […]

Cluster analysis is a technique for statistical data analysis that is widely used in many fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics. Clustering is to divide similar objects into different groups or more subsets through static classification methods, so that member objects in the same subset have [...]

Clustering ensemble is an algorithm to improve the accuracy, stability and robustness of clustering results. By integrating multiple base clustering results, a better result can be produced. The basic idea of this method is to cluster the original data set with multiple independent base clusterers, and then use some ensemble method to process it and obtain the best […]

A component of the decoder in a digital remote control system. It consists of a bistable trigger and a coding switch. Each bistable has two states, "1" and "2". When n bistables are cascaded, there are 2n possible combinations. Each combination is a binary code group. The coding switches are connected according to the binary code group. The purpose of the coding matrix is to convert the command […]

One of the conferences on computational learning theory, hosted by ACM and held annually. Computational learning theory can be seen as the intersection of theoretical computer science and machine learning, so it is widely considered a computer science-related conference. Official website: https://learningtheory.org/colt2019 […]

Competitive learning is a learning method of artificial neural networks. When the network structure is fixed, the learning process is reduced to modifying the connection weights. Competitive learning refers to the competition among all units in the network unit group for the right to respond to external stimulus patterns. The connection weights of the winning units change in a direction that is more favorable to the competition for this stimulus pattern.

Component learners are a type of individual learners, which are based on individual learners generated by ensemble learning. When individual learners are generated by different learning algorithms, it is called a heterogeneous ensemble, and these individual learners are called component learners.

Interpretability means that when you need to understand or solve a problem, you can get the relevant information you need. Interpretability at the data level: Let the neural network have a clear symbolic internal knowledge expression to match the human knowledge framework, so that people can diagnose and modify the neural network at the semantic level. Interpretability of machine learning[…]

Classification algorithms in the field of machine learning classify attributes into discrete and continuous ones, where discrete attributes have finite or infinite countable values that may or may not be represented by integers, for example, the attributes hair_color , smoker , medical_test , and drink_size all have finite values […]

Definition Cascade Correlation is a supervised learning architecture that can be used to build a minimal multi-layer network topology. Its advantage is that the user does not need to worry about the topology of the network, and its learning speed is faster than traditional learning algorithms. Correlation Algorithm The Cascade Correlation algorithm is implemented in the following way: Start with a minimal network that only contains input and output […]

Definition Under specified conditions, using a reference standard to assign values to the characteristics of measuring instruments, including reference materials, and determine their indication error. Purpose To determine the indication error and determine whether it is within the expected tolerance range; To obtain a reported value of the deviation from the nominal value, which can be used to adjust the measuring instrument or correct the indication; To give any […]

Definition For the differential equation $latex \frac{d \mathbf{x}}{dt}=\mathbf{f}(t, \mathbf{x}), \mathbf{x} \in \mathbb{R}^{n}$ , if $latex […]