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pojęcia
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Conditional Random Field

Discriminative statistical model used for structured prediction, which directly models the conditional probability P(y|x) of labels y given observations x.

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Potential Factor

Non-negative function that quantifies the affinity between variable configurations in a CRF, transformed into probabilities by normalization.

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Energy Function

Function that assigns a scalar value to each possible variable configuration, where low energy configurations correspond to high probabilities.

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Linear-Chain CRF

Specialized type of CRF where output variables form a linear chain, widely used for sequence labeling like NER or POS tagging.

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Partition Function

Partition constant that ensures the sum of probabilities of all possible configurations equals 1, calculated as the sum of exponentials of energies.

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Conditional Likelihood

Objective function maximized during CRF training, measuring the probability of correct labels given training observations.

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Message Passing

Inference algorithm that propagates information between nodes in a factor graph to compute marginal beliefs or MAP assignments.

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High-Order CRF

Extension of linear-chain CRFs that models dependencies between non-adjacent variables in the sequence, capturing more complex relationships.

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Décodage MAP

Processus d'inférence qui cherche la configuration d'états maximisant la probabilité a posteriori (Maximum A Posteriori) dans un CRF.

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CRF-Semi-Cache

Variante de CRF où les états peuvent être partiellement observés, combinant apprentissage supervisé et non-supervisé pour améliorer la performance.

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Facteur de Transition

Composant du CRF qui modélise la probabilité de transition entre états successifs dans une séquence, capturant les dépendances temporelles.

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