Classification of finite sequences without explicit knowledge of their statistical nature is a fundamental problem with many important applications.
We design a method for the classification of discrete sequences whenever they can be compressed. We introduce the method and illustrate its application.
Agnostic classification of Markovian sequences · Contents. NIPS '97: Proceedings of the 1997 conference on Advances in neural information processing systems 10.
Classification of finite sequences without explicit knowledge of their statistical nature is a fundamental problem with many important applications.
Classification of finite sequences without explicit knowledge of their statistical nature is a fundamental problem with many important applications.
Classification of finite sequences without explicit knowledge of their statistical origins is of great importance to various applications, ...
Sep 11, 2024 · HMMs offer significant advantages in scenarios with imbalanced or smaller datasets due to their simplicity, interpretability, and efficiency.
Missing: Markovian | Show results with:Markovian
In this paper, we introduce a novel classification scheme for sequences based on HMMs, which is obtained by extending the recently proposed similarity-based ...
Specifically, Active-ILESS achieves the same label complexity as Agnostic CAL, while being simpler in the sense that its consumption of ERM computations is ...
Missing: Markovian | Show results with:Markovian
Agnostic classification of Markovian sequences. R El-Yaniv, S Fine, N Tishby. Advances in Neural Information Processing Systems 10, 1997. 70, 1997. Harnessing ...