Glossary

**Hard Negative Mining**: A technique used in machine learning, particularly in object detection, where the model is trained on difficult examples that are often misclassified, helping to improve model accuracy.

**Hashing**: A process that converts data into a fixed-size string of characters, which typically represents the data in a compressed form, often used in data retrieval and cryptography.

**Heuristic**: A problem-solving approach that uses practical methods or rules of thumb to find a satisfactory solution, often faster than traditional methods but without guaranteed optimality.

**Hierarchical Clustering**: A method of cluster analysis that seeks to build a hierarchy of clusters, starting by treating each data point as its own cluster and then merging the closest pairs iteratively.

**Hidden Markov Model (HMM)**: A statistical model used to represent systems that are modeled as a Markov process with hidden states, often used in speech recognition, bioinformatics, and finance.

**High-Dimensional Data**: Data with a large number of features or dimensions, which can pose challenges for machine learning algorithms due to the curse of dimensionality.

**Holdout Method**: A technique used to evaluate the performance of a machine learning model by splitting the dataset into a training set and a test set, where the model is trained on the training set and evaluated on the test set.

**Homogeneous Ensemble**: An ensemble learning technique that combines multiple models of the same type, such as multiple decision trees, to improve the robustness and accuracy of predictions.

**Hyperparameter**: A parameter whose value is set before the learning process begins, governing aspects of the training process, such as learning rate, number of layers, or batch size.

**Hyperparameter Tuning**: The process of searching for the best hyperparameters for a machine learning model, often using methods like grid search or random search.

**Hybrid Model**: A model that combines two or more different machine learning techniques, such as combining a neural network with a decision tree, to leverage the strengths of each method.

**Hinge Loss**: A loss function commonly used in machine learning, particularly with support vector machines (SVMs), that penalizes misclassified data points based on their margin of separation from the decision boundary.

**Histogram of Oriented Gradients (HOG)**: A feature descriptor used in computer vision and image processing for object detection, which captures the distribution of gradient orientations in localized portions of an image.

**Hyperplane**: A decision boundary in a high-dimensional space that separates data points into different classes, commonly used in linear classifiers like support vector machines (SVMs).

**Harmonic Mean**: A type of average often used in machine learning to combine precision and recall into the F1 score, calculated as the reciprocal of the average of the reciprocals of the values.

**Hierarchical Data**: Data that is organized in a tree-like structure with parent-child relationships, often used in databases, organizational structures, and taxonomies.

**Hyperbolic Tangent (tanh)**: A sigmoid-like activation function used in neural networks that maps input values to outputs between -1 and 1, often preferred over the sigmoid function for its zero-centered output.

**Hypothesis Testing**: A statistical method used to determine whether there is enough evidence to reject a null hypothesis, often used in A/B testing and experimental design.

**Heteroscedasticity**: A condition in regression analysis where the variance of the errors varies across observations, often requiring special treatment to produce valid model estimates.

**Hidden Layer**: Layers in a neural network that lie between the input and output layers, where the network learns internal representations or features of the input data.

**Hierarchical Attention Network (HAN)**: A neural network architecture that applies attention mechanisms at multiple levels, such as word-level and sentence-level, often used in text classification tasks.

**Human-in-the-Loop (HITL)**: An approach in AI and machine learning where human feedback is integrated into the training process, often used to improve model accuracy and handle complex tasks.

**Hawkes Process**: A mathematical model used to describe self-exciting point processes, often used in financial modeling and event prediction.

**Heaviside Step Function**: A discontinuous function that is zero for negative input and one for positive input, often used in mathematical models of binary decisions.

**Hamming Distance**: A metric used to measure the difference between two strings of equal length by counting the number of positions at which the corresponding symbols differ, often used in error detection and correction.

**Haar Wavelet**: A type of wavelet used in signal processing that provides a simple and fast method for hierarchical decomposition, often used in image compression and feature extraction.

**Hidden Unit**: A neuron or node in a hidden layer of a neural network, responsible for learning intermediate features of the input data.

**Hopfield Network**: A type of recurrent neural network with binary threshold nodes, often used for associative memory and optimization problems.

**Hinge Embedding Loss**: A loss function used in machine learning, particularly in embedding learning, that measures the distance between pairs of data points with the goal of preserving the relative distances between points in the embedding space.

**Heterogeneous Ensemble**: An ensemble learning technique that combines models of different types, such as a decision tree and a neural network, to improve overall model performance.

**Hurst Exponent**: A statistical measure used to assess the long-term memory of time series data, often used in finance and signal processing to detect trends and mean reversion.

**Hierarchical Reinforcement Learning**: A type of reinforcement learning that decomposes a complex task into a hierarchy of simpler subtasks, enabling more efficient learning and better generalization.

**Hybrid Recommender System**: A recommendation system that combines multiple recommendation techniques, such as collaborative filtering and content-based filtering, to improve recommendation accuracy and diversity.

**Hot Encoding**: A misnomer often confused with "one-hot encoding," a process where categorical variables are converted into a binary matrix representation, with a single "hot" bit indicating the presence of a particular category.

**Hopfield Model**: A type of recurrent artificial neural network that serves as a content-addressable memory system with binary threshold units, often used in optimization and pattern recognition.

**Hierarchical Softmax**: An efficient approximation of the softmax function used in large-scale classification tasks, where the computation is divided into a hierarchy of smaller softmax functions, reducing the computational cost.

**Hinton Diagram**: A graphical representation of matrix values using colored squares, often used to visualize the weights of a neural network.

**Hazard Function**: A function that describes the instantaneous rate of occurrence of an event at a given time, often used in survival analysis and reliability engineering.

**Human Pose Estimation**: A computer vision task that involves detecting and predicting the positions of human joints (pose) in an image or video, often used in motion capture, sports analysis, and human-computer interaction.

**Hypercomplex Numbers**: An extension of complex numbers used in advanced mathematics and physics, often employed in quaternion-based neural networks for tasks like 3D rotation and signal processing.

**Hierarchical Bayesian Model**: A statistical model that includes multiple levels of random variables, allowing for the modeling of complex data structures with dependencies across different levels.

**Histogram Matching**: An image processing technique used to adjust the histogram of an image to match the histogram of a reference image, often used in image normalization and contrast adjustment.

**Hypergraph**: A generalization of a graph where edges can connect any number of vertices, often used in complex network analysis and machine learning tasks involving higher-order relationships.

**Hierarchical Mixture Model**: A probabilistic model that represents data as a mixture of distributions at multiple levels of hierarchy, often used in clustering and density estimation.

**Histogram Equalization**: An image processing technique used to improve the contrast of an image by redistributing the intensity values, often used to enhance details in images with poor lighting conditions.

**Hypernetwork**: A neural network that generates the weights for another network, often used to improve the efficiency and flexibility of model adaptation and transfer learning.

**Hierarchical Softmax**: A computationally efficient approximation of the softmax function used in large-scale classification tasks, where the output space is structured hierarchically to reduce the number of required computations.

**Hessian Matrix**: A square matrix of second-order partial derivatives of a function, often used in optimization algorithms to assess the curvature of the objective function.

**Hidden State**: In recurrent neural networks, the hidden state is a vector that holds information about previous inputs, capturing temporal dependencies in sequence data.

**Hybrid Optimization**: An optimization approach that combines different optimization algorithms, such as gradient-based methods with evolutionary algorithms, to leverage the strengths of each method.

**Hamming Code**: A type of error-correcting code that can detect and correct single-bit errors in transmitted data, often used in digital communication systems to ensure data integrity.