Model Selection of Sequence Prediction Algorithms by Compression
; Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:160-163, 2017.
This paper describes estimating performance of sequence prediction algorithms and hyperparameters by compressing the training dataset itself with the probablities predicted by the trained model. With such estimation we can automate the selection and tuning process of learning algorithms. Spectral learning algorithm are experimented with.