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Notas para hacer una tesis genial

Metodología

CRF

  • Modelo gráfico a utilizar: TODO: Investigar esto The 1st-order Markov CRF with state and transition features (dyad features). State features are conditioned on combinations of attributes and labels, and transition features are conditioned on label bigrams.

  • Algorítmo de aprendizaje: lbfgs: Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method.

Maximizar el logaritmo de la verosimilitud de los datos de entrenamiento con los términos de regulación L1 y/o L2 usando el método L-BFGS. Cuando un coeficiente L1 es especificado en cero, el algoritmo cambia al método Orthant-Wise Limited-memory Quasi-Newton (OWL-QN). Prácticamente, este algoritmo mejora los pesos de características muy lento al inicio del proceso de entrenamiento, pero converge a los pesos de características optimos rápidamente al final

  • Hiperparámetros:
    • c1=VALUE The coefficient for L1 regularization. If a non-zero value is specified, CRFsuite switches to the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. The default value is zero (no L1 regularization).
    • c2=VALUE The coefficient for L2 regularization. The default value is 1.
    • max_iterations=NUM The maximum number of iterations for L-BFGS optimization. The L-BFGS routine terminates if the iteration count exceeds this value. The default value is set to the maximum value of integer on the machine (INT_MAX).
    • num_memories=NUM The number of limited memories that L-BFGS uses for approximating the inverse hessian matrix. The default value is 6.
    • epsilon=VALUE The epsilon parameter that determines the condition of convergence. The default value is 1e-5.
    • stop=NUM The duration of iterations to test the stopping criterion. The default value is 10.
    • delta=VALUE The threshold for the stopping criterion; an L-BFGS iteration stops when the improvement of the log likelihood over the last ${stop} iterations is no greater than this threshold. The default value is 1e-5.
    • linesearch=STRING The line search method used in the L-BFGS algorithm. Available methods are: "MoreThuente" (MoreThuente method proposd by More and Thuente), "Backtracking" (Backtracking method with regular Wolfe condition), and "StrongBacktracking" (Backtracking method with strong Wolfe condition). The default method is "MoreThuente".
    • max_linesearch=NUM The maximum number of trials for the line search algorithm. The default value is 20.