دانلود کتاب مدل ها و برنامه های پنهان مارکوف

The quantity and diversity of generated data (text, images, videos, audio files, etc.) continue to increase. This has been mainly fueled by the increasing use of social media platforms and the integration of low-cost sensors in a variety of digital systems. The extraction of useful knowledge from these data and its modeling has been the topic of extensive research in the past, and many advanced data mining and machine learning techniques and tools have been developed over the years. Since roughly 15 years, much has changed in the field of machine learning. The most spectacular developments have been in the area of deep learning which has shown state-of-the-art results in many challenging applications. Recent advances have been spectacular and have captured much popular imagination. At the same time other machine learning approaches continue to evolve. This is especially true in the case of learning models dedicated to sequential data. Hidden Markov Models (HMMs) are one of the most fundamental and largely applied statistical tools for modeling sequential data.