Forgetting Superfluous Information in Supervised Pattern Recognition Systems with Ongoing Learning
نویسندگان
چکیده
Ongoing learning refers to the possibility of a system to increase knowledge from the experience obtained when working in the classification of new patterns. In this paper, we present an automatic classification system with ongoing learning capabilities and analyze the importance of using some size reduction algorithm to remove redundant training patterns.
منابع مشابه
Learning and Forgetting with Local Information of New Objects
The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to incorporate new knowledge from the experience obtained when classifying new objects and correspondingly, to improve the efficiency of the system. We employ a stochastic rule for classifying and editing, along with a conde...
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 کاملLearning with Forgetting: an Approach to Achieve Adaptive Neural Networks
Much work on modelling pattern recognition by AI systems has focused on the stability and plasticity of a system’s ongoing response to novel inputs. This paper discusses a general learning mechanism for ART2 neural networks, which incorporates forgetting by long-term memory trace decay. Such approach enables the system to reuse memory resources and adapt to a huge in diversity or continually ch...
متن کاملPattern classification and clustering: A review of partially supervised learning approaches
The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learning (PSL) task. Special emphasis is put on the fields of pattern recognition and clustering involving partially (or, weakly) labeled data sets. The major instances of PSL techniques are categorized into the following taxonomy: (i) active learning for training set design, where the learning algorit...
متن کاملPattern Recognition: Possible Research Areas and Issues
Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervise...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004