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AUC is defined as the area under the ROC curve and the coordinate axis. Since the ROC curve is above y=x, the value range of AUC is between 0.5 and 1. AUC can be used as an indicator of the quality of the model when comparing different classification models. Its main significance lies in AU […]

The Hessian matrix, also known as the Hessian matrix or the Hessian matrix, is a block matrix consisting of the second-order partial derivatives of multivariate real-valued functions. This is a generalization of the second-order derivative to multivariate functions and is closely related to the extreme values of the function.

Alpha-beta pruning is a search algorithm used to reduce the number of nodes in the Minimax search tree.

Adaptive resonance theory (ART for short) refers to a theoretical model that states that when there is interaction between a neural network and the environment, the encoding of environmental information will spontaneously occur in the neural network, and the network can self-organize to generate the encoding of environmental knowledge.

Application-specific integrated circuits, or ASICs for short, are integrated circuits with special specifications that are customized according to different product requirements; on the contrary, non-customized ones are application-specific standard product (ASSP) integrated circuits.

An intelligent agent refers to a software or hardware entity that can act autonomously. It has been translated as "agent", "agent", "intelligent subject", etc.

The original sampling method is a basic sampling method for directed graph models, which refers to generating samples from the joint distribution represented by the model, also known as the ancestral sampling method.

Anomaly detection is to find objects that are different from most objects, in fact, it is to find outliers. Anomaly detection is sometimes also called deviation detection. Abnormal objects are relatively rare.

The learning rule is a concept in neural network models that represents how the weights in the network are adjusted over time. This is generally viewed as a long-term dynamical rule.

The actor-critic algorithm is a reinforcement learning algorithm that combines a policy network and a value function. It uses the reward and punishment information of the results to calculate the probability of taking various actions under different states. It is also called the AC algorithm.

The task of the acoustic model is to calculate P(O|W), which is the probability of generating a speech waveform for the model. The acoustic model is one of the most important parts of the speech recognition system. It accounts for most of the computational overhead of speech recognition and determines the performance of the speech recognition system.

The adaptive bitrate algorithm is a video transmission technology that automatically adjusts the streaming media bitrate. The adjustment factors mainly depend on the network conditions or client delay.

The Tensor Processing Unit (TPU) is a special-purpose integrated circuit developed specifically for machine learning.

Oblique decision tree is also called multivariate decision tree. It is a decision tree in which the nodes use linear expressions of multiple attributes as the evaluation criteria.

Unordered attributes are attributes that cannot be arranged in order.

Restricted isometry property (RIP) is a property used to describe the relationship between nearly orthogonal matrices when dealing with problems such as sparse vectors.

Training examples refer to instances that are marked for training during the training process.

The support vector expansion is the expansion of the kernel function of the model's optimal solution through the training samples.

Sparsity refers to a situation where the proportion of 0 elements is large.

The state characteristic function is a characteristic function defined on the node and depends on the current position.

The True Prediction Rate (TPR) is the ratio of the number of positive sample predictions to the actual number of positive samples.

The true class refers to those samples that are correctly judged as the positive class in the binary classification problem.

True negatives (TN) refer to those samples that are correctly judged as negative in a binary classification problem.

Transductive learning is a method of predicting specific test samples by observing specific training samples.