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Groups data into clusters based on similarity without pre-labeled categories. Applied in image segmentation and object categorization.

An ensemble learning method constructing multiple decision trees during training and outputting the class that is the mode of the classes of the individual trees. Employed in scene labeling and detection tasks.

Statistical model where the system being modeled is assumed to be a Markov process with hidden states. Used in speech and handwriting recognition.

Supervised learning model that finds the hyperplane maximizing the margin between classes. Used for classification and regression tasks.

Identifies unique features within an image and uses them to compare different images. Common in image stitching and 3D reconstruction.

Applies Bayes' theorem with strong independence assumptions between the features. It's applied in spam detection and medical diagnosis.

Compares a template image to an input image, aiming to detect instances of the template. Used in object detection and quality control.

A model used for image classification, based on the idea of representing images through the histogram of visual words. Effective in texture and object recognition.

Computational models inspired by the human brain, capable of learning patterns from data. Essential in facial recognition and autonomous vehicles.

Tree-like model used for decision making by creating a flowchart-like structure. Utilized in object classification and image categorization.

A statistical process that uses orthogonal transformation to convert a set of observations into a set of linearly uncorrelated variables. Applied in face recognition and dimensionality reduction.