Structured Prediction
I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than discrete or real values.
Similar to commonly used supervised learning techniques, structured prediction models are typically trained by means of observed data in which the predicted value is compared to the ground truth, and this is used to adjust the model parameters. Due to the complexity of the model and interrelations of predicted variables, the processes of model training and inference are often computationally infeasible, so approximate inference and learning methods are used.
- Ground truth is information that is known to be real or true, provided by direct observation and measurement as opposed to information provided by inference.
- Approximate inference methods make it possible to learn realistic models from big data by trading off computation time for accuracy, when exact learning and inference are computationally intractable.
An example application is the problem of translating a natural language sentence into a syntactic representation such as a parse tree. This can be seen as a structured prediction problem in which the structured output domain is the set of all possible parse trees. Structured prediction is used in a wide variety of domains including bioinformatics, nlp, speech recognition, and computer vision.
A parse tree or parsing tree is an ordered, rooted tree that represents the syntactic structure of a string according to some context-free grammar. The term parse tree itself is used primarily in computational linguistics; in theoretical syntax, the term syntax tree is more common.
Probabilistic graphical models form a large class of structured prediction models. In particular, Bayesian networks and random fields are popular.
- A Bayesian network is a graphical model that represents the set of variables and their conditional dependencies via a directed acyclic graph (SAG).
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