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We have compiled hundreds of related entries to help you understand "artificial intelligence"
Neighborhood Component Analysis
Neighborhood Component Analysis
Neighbor Component Analysis (NCA) is a distance measurement learning method associated with KNN (K Nearest Neighbors), which belongs to supervised learning methods. It was first proposed by Goldberger et al. in 2004.
2 years ago
Within-class Scatter Matrix
Within-class Scatter Matrix
The intra-class scatter matrix represents the scatter of each sample point around the mean.
2 years ago
Understandability
Understandability
Comprehensibility refers to how easy something is to understand, mainly whether it is easy for readers to understand.
2 years ago
Polarity Detection
Polarity Detection
Polarity detection is the process of classifying the sentiment polarity of text in natural language.
3 years ago
Activity Rule
Activity Rule
The activation function is a dynamic principle that is often used in neural network models. It defines how a neuron changes its activation value based on the activity of other neurons. The general activation function depends on the weights in the network, which can introduce nonlinear factors and is often used to solve problems that cannot be solved with linear equations.
3 years ago
Parse Tree
Parse Tree
A parse tree, also called a concrete syntax tree, is a representation of the results of a syntax analysis, which represents the grammatical structure of a language in a tree shape.
3 years ago
Architecture
Architecture
Structure is a method of displaying a neural network topology diagram, which is often used in the field of neural networks. In a neural network, variables can be the weights and incentive values of neuron connections.
3 years ago
Analytical Gradients
Analytical Gradients
Analytical gradient refers to the use of backpropagation in neural network algorithms to calculate the gradient of the objective function with respect to each parameter.
3 years ago
Approximation
Approximation
Approximation or approximation means that one thing is similar to another thing, but not exactly the same.
3 years ago
Approximate Bayesian Computation
Approximate Bayesian Computation
Approximate Bayesian Computation (ABC) is a computational method based on Bayesian statistics that can be used to estimate the posterior distribution of model parameters.
3 years ago
Approximate Inference
Approximate Inference
Approximate inference methods refer to sampling and learning from a large amount of data and using hypothesis-verification logic to continuously approach the true model.
3 years ago
Affinity Matrix
Affinity Matrix
In mathematics, a distance matrix is a matrix (i.e. a two-dimensional array) containing the distances between pairs of points.
3 years ago
Plug and Play Generative Networks
Plug and Play Generative Networks
Plug and Play Generative Network (PPGN) is a model proposed by Nguyen et al. in 2016.
3 years ago
Nominal Attribute
Nominal Attribute
The column name attribute refers to the 'name-related' feature of the data, and the corresponding value is the name of some symbol or thing.
3 years ago
Accumulated Error Backpropagation
Accumulated Error Backpropagation
Cumulative error back propagation is a neural network algorithm that uses a gradient descent-based strategy to adjust parameters in the negative gradient direction of the target, with the goal of minimizing the training error. It is also called the "back propagation algorithm", or "BP algorithm" for short.
3 years ago
Clustering
Clustering
Grouping related samples together is generally used for unsupervised learning. Once all samples are grouped, researchers can optionally assign meaning to each cluster. There are many clustering algorithms, for example, the k-means algorithm clusters samples based on their proximity to the centroid, as shown below: After that, researchers can […]
3 years ago
Majority Voting
Majority Voting
The majority voting method is a voting method that requires more than half of the valid votes to be recognized. When multiple classifiers predict a certain category, only the part that is higher than half of the total results will be predicted. The following is the formula for representation: $latex {H{ \left( {x} \right) }\text{ […]
3 years ago
Manifold Learning
Manifold Learning
Manifold learning is a basic method in pattern recognition. It seeks the essence of things and the inherent laws of data generation based on observed phenomena. Manifold learning is divided into two types: linear manifold learning algorithm and nonlinear manifold learning algorithm. Nonlinear manifold learning algorithm includes isomap, Laplace eigenmap, and L[…]
3 years ago
Mean Squared Error
Mean Squared Error
The mean square error is the expected value that reflects the difference between the estimated value and the true value. It is often used to evaluate the degree of change in data and predict the accuracy of data. Assuming that there is a parameter , and its estimation function is
, then $latex {MSE [...]
3 years ago
Machine Translation
Machine Translation
Machine translation is the use of computers to convert between different languages, usually translating from a source language into a target language. Translation Process From the perspective of human translation, the translation process can be broken down into the following: Decipher the meaning of the source text and recompile the meaning obtained after the analysis into the target language. Translation Methods General steps of machine translation […]
3 years ago
Manifold Assumption
Manifold Assumption
The manifold hypothesis is a common hypothesis in semi-supervised learning, and the other is the clustering hypothesis. The manifold hypothesis refers to examples with similar properties, which are usually in a small local area, so the labels are also very similar. This hypothesis reflects the local smoothness of the decision function. Unlike the clustering hypothesis that focuses on the overall characteristics, the manifold hypothesis focuses more on the model’s […]
3 years ago
Maximum Likelihood Estimation
Maximum Likelihood Estimation
Maximum likelihood estimation is an estimation method mainly used to determine model parameters by finding possible data that maximizes the model observation value and using it as the final choice. In maximum likelihood estimation, sampling satisfies the assumption of independent and identical distribution. Its purpose is to use known sample results to infer the parameter value that has the highest probability of leading to related results.
3 years ago
Lazy Learning
Lazy Learning
Lazy learning is a training set processing method that trains at the same time as receiving the test sample. In contrast, eager learning starts learning samples during the training phase. If the task data changes frequently, lazy learning can be used. No training is performed first, and the prediction request is received before the current data is estimated. […]
3 years ago
Learning by Analogy
Learning by Analogy
Analogy learning is a method of cognitive thinking and inference. It compares two types of things or situations to find their similarities at the object level, and then compares the relationship between things and situations based on this, and then obtains the corresponding solution by appropriately sorting/exchanging them to another thing. There are several classification methods for analogy learning […]
3 years ago