scholarly journals Context-sensitive learning methods for text categorization

1996 ◽  
Author(s):  
William W. Cohen ◽  
Yoram Singer
Keyword(s):  
Text Categorization ◽  
Learning Methods ◽  
Context Sensitive

scholarly journals Discretization for Continuous Attributes

2011 ◽  
pp. 397-402 ◽  
Author(s):  
Fabrice Muhlenbach ◽  
Ricco Rakotomalala
Keyword(s):  
Text Categorization ◽  
The Elderly ◽  
Representation Space ◽  
Learning Method ◽  
Learning Methods ◽  
Computation Process ◽  
Human Interpretation ◽  
Discrete Values ◽  
A Minor ◽  
Continuous Attribute

In the data-mining field, many learning methods — such as association rules, Bayesian networks, and induction rules (Grzymala-Busse & Stefanowski, 2001) — can handle only discrete attributes. Therefore, before the machine-learning process, it is necessary to re-encode each continuous attribute in a discrete attribute constituted by a set of intervals. For example, the age attribute can be transformed in two discrete values representing two intervals: less than 18 (a minor) and 18 or greater. This process, known as discretization, is an essential task of the data preprocessing not only because some learning methods do not handle continuous attributes, but also for other important reasons. The data transformed in a set of intervals are more cognitively relevant for a human interpretation (Liu, Hussain, Tan, & Dash, 2002); the computation process goes faster with a reduced level of data, particularly when some attributes are suppressed from the representation space of the learning problem if it is impossible to find a relevant cut (Mittal & Cheong, 2002); the discretization can provide nonlinear relations — for example, the infants and the elderly people are more sensitive to illness. This relation between age and illness is then not linear — which is why many authors propose to discretize the data even if the learning method can handle continuous attributes (Frank & Witten, 1999). Lastly, discretization can harmonize the nature of the data if it is heterogeneous — for example, in text categorization, the attributes are a mix of numerical values and occurrence terms (Macskassy, Hirsh, Banerjee, & Dayanik, 2001).

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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