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Parameter estimation refers to estimating the overall indicator using the sample indicator. Specifically, it is estimating the overall mean using the sample mean or estimating the overall rate using the sample rate.

POS tagging is the process of classifying and labeling words in a sentence. It is the process of assigning a POS tag to each word based on the components it plays in syntactic structure or language morphology.

The semi-naive Bayes classifier is a classification method that takes into account the interdependence between some attributes. It is a relaxation strategy when the naive Bayes classifier features are difficult to be independent of each other.

Semi-supervised learning is a learning technique between supervised learning and unsupervised learning. It uses both labeled and unlabeled samples for learning.

A saddle point is a stationary point that is not a local extreme point.

The version space is the subset of all hypotheses in concept learning that are consistent with the known dataset and is often used to converge on content.

Word sense disambiguation (WSD) is semantic disambiguation at the word level.

Residual network (ResNet) is based on a simple network, with a shortcut connection inserted to convert the network into its corresponding residual version. The residual network does not directly fit the target, but fits the residual.

The representation theorem is a theorem in statistical learning that states that the minimum of a regularized risk function defined on a reproducing kernel Hilbert space can be represented as a linear combination of the input points in the training set.

Semi-supervised support vector machine (S3VM) is a generalization of support vector machine in semi-supervised learning.

Word embedding is a general term for language models and representation learning techniques in natural language processing (NLP).

Word sense disambiguation (WSD) is semantic disambiguation at the word level. It is an open problem in natural language processing and ontology. Ambiguity and disambiguation are the core issues in natural language understanding. At the word meaning, sentence meaning, and paragraph meaning levels, there will be different semantics of the language depending on the context. Disambiguation refers to the process of determining the semantics of an object based on the context.

Tokenization, also known as lexical analysis, is the process of converting characters (for example, in a computer program or a web page) into tokens (strings of characters with assigned and therefore identified meanings).

Variational inference uses a known distribution to adjust it to fit the distribution we need but is difficult to express in a formula.

A reference model is a model used as a benchmark and comparison. In the definition of the Organization for the Promotion of Structured Information Standards, it is used to understand the important relationships between entities in some environment and to develop a general standard or specification framework to support that environment. Concept Summary: Reference models are used to provide information about an environment and to describe […]

The re-weighting method means that in each round of the training process, a weight is re-assigned to each training sample according to the sample distribution.

Marginal distribution refers to the probability distribution of only some variables in a multidimensional random variable in probability theory and statistics. Definition Assume there is a probability distribution related to two variables: $latex P(x, y) $ The marginal distribution about one of the specific variables is the conditional probability distribution given the other variables: $lat […]

Marginalization is a method of discovering another variable based on a variable. It determines the marginal contribution of another variable by summing up the possible values of the variable. This definition is relatively abstract, and the following uses relevant cases to describe it. Suppose you need to know the impact of weather on the happiness index, you can use P (happiness | weather) to represent it, that is, given a weather category […]

Hierarchical clustering is a collection of algorithms that form nested clusters by merging from bottom to top or splitting from top to bottom. This hierarchical class is represented by a "dendrogram", and the Agglomerative Clustering algorithm is one of them. Hierarchical clustering attempts to […]

Game theory, also known as strategy theory, game theory, etc., is not only a new branch of modern mathematics, but also an important discipline in operations research. It mainly studies the interaction between incentive structures, considers the predicted and actual behaviors of individuals in the game, and studies related optimization strategies. Game behavior refers to behaviors that are competitive or confrontational in nature. In such behaviors, […]

The Extreme Learning Machine is a neural network model in the field of machine learning. It can be used to solve single hidden layer feedforward neural networks. Unlike traditional feedforward neural networks (such as BP neural networks) that require a large number of training parameters to be set manually, the Extreme Learning Algorithm only needs to set the network structure without setting other parameters. Therefore, it is simple and easy to use.

The error rate refers to the proportion of prediction errors in the prediction. The calculation formula is generally: 1 – Accuracy (%) The trained model can generally be used to measure the error rate of a model in a data set. Three numbers are important: Bayes Optimal Error: The ideal […]

Precision is a metric used in information retrieval and statistical classification. It refers to the ratio of the correct samples extracted to the number of samples extracted.

Representation learning, also known as representation learning, is the use of machine learning technology to automatically obtain the vectorized expression of each entity or relationship, so that it is easier to extract useful information when building classifiers or other predictive variables.