Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
Hello, thank you for sharing your work! I am new to Tsetlin; please forgive me if my questions are a bit naive.
Have you suggestions or examples of how best to represent data where the sequence of features to be considered for clauses have importance? For example, feature3 must be followed by feature4 for a condition to hold True?
Related, could I use a Tsetlin to discover string patterns when I have not examined the dataset?
Hello, thank you for sharing your work! I am new to Tsetlin; please forgive me if my questions are a bit naive.
Have you suggestions or examples of how best to represent data where the sequence of features to be considered for clauses have importance? For example, feature3 must be followed by feature4 for a condition to hold True?
Related, could I use a Tsetlin to discover string patterns when I have not examined the dataset?
Thank you.