Modeling of Abrasion Resistance Performance of Persian Handmade Wool Carpets Using Artificial Neural Network
This paper presents the application of Artificial Neural Network (ANN) modeling for the prediction of abrasion resistance of Persian handmade wool carpets. Four carpet constructional parameters, namely knot density, pile height, number of ply in pile yarn and pile yarn twist have been used as input parameters for ANN model. The prediction performance was judged in terms of statistical parameters like correlation coefficient (R) and Mean Absolute Percentage Error (MAPE). Though the training performance of ANN was very good, the generalization ability was not up to the mark. This implies that large number of training data should be used for the adequate training of ANN models.
We study the problem of learning a kernel matrix from an apriori kernel and training data. An unconstrained convex optimization formulation is proposed, with an arbitrary convex smooth loss function on kernel entries and a LogDet divergence for regularization. Since the number of variables is of order O(n), standard Newton and quasi-Newton methods are too time-consuming. An operator form Hessian is used to develop an O(n) trust-region inexact Newton method, where the Newton direction is computed using several conjugate gradient steps on the Hessian operator equation. On the uspst dataset, our algorithm can handle 2 million optimization variables within one hour. Experiments are shown for both linear (Mahalanobis) metric learning and for kernel learning. The convergence rate, speed and performance of several loss functions and algorithms are discussed.