Activity recognition evaluation via machine learning
نویسندگان
چکیده
منابع مشابه
Machine Learning for Activity Recognition
This paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give a general criteria set to evaluate activity recognition methods. I sum...
متن کاملQuantum phase recognition via unsupervised machine learning
The application of state-of-the-art machine learning techniques to statistical physic problems has seen a surge of interest for their ability to discriminate phases of matter by extracting essential features in the many-body wavefunction or the ensemble of correlators sampled in Monte Carlo simulations. Here we introduce a generalization of supervised machine learning approaches that allows to ...
متن کاملMachine learning based Visual Evoked Potential (VEP) Signals Recognition
Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...
متن کاملCross-domain activity recognition via transfer learning
In activity recognition, one major challenge is how to reduce the labeling effort one needs to make when recognizing a new set of activities. In this paper, we analyze the possibility of transferring knowledge from the available labeled data on a set of existing activities in one domain to help recognize the activities in another different but related domain. We found that such a knowledge tran...
متن کاملEnsemble Optical Character Recognition Systems via Machine Learning
Optical Character Recognition (OCR) Systems are widely used to process scanned text into text usable by computers. We observe that current OCR systems have bad performance on domain-specific papers, even generating lots of incorrect words; besides, different OCR systems make relatively independent mistakes. Based on these observations, we train an ensemble system from multiple open-source OCR s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ICST Transactions on Ambient Systems
سال: 2019
ISSN: 2032-927X
DOI: 10.4108/eai.23-3-2018.161436