Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?
نویسندگان
چکیده
منابع مشابه
Supervised ANN vs. Unsupervised SOM to Classify EEG Data for BCI: Why can GMDH do better?
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back-prop and...
متن کاملComparing Supervised vs. Unsupervised Image Segmentation Methods
This project compares the supervised logistic regression segmentation algorithm against the unsupervised k-means clustering segmentation. We observed that the difference between either method is not very significant. When performed on the 100 test cases for BSD300, the supervised method on average achieved a precision rate of 0.47 and the unsupervised method achieved a precision rate of 0.41. T...
متن کاملHost Specificity Testing: Why Do We Do It and How We Can Do It Better
Host specificity testing is universally used in weed biological control to predict nontarget effects of potential agents. Despite this, there is some confusion regarding the role of host specificity testing in making such predictions. One possible role is as an assay of field host range. In this case, the ideal host specificity test will simulate conditions encountered in the field, and the res...
متن کاملUnsupervised vs. supervised weight estimation for semantic MT evaluation metrics
We present an unsupervised approach to estimate the appropriate degree of contribution of each semantic role type for semantic translation evaluation, yielding a semantic MT evaluation metric whose correlation with human adequacy judgments is comparable to that of recent supervised approaches but without the high cost of a human-ranked training corpus. Our new unsupervised estimation approach i...
متن کاملSupervised vs. Unsupervised Learning for Intentional Process Model Discovery
Learning humans’ behavior from activity logs requires choosing an adequate machine learning technique regarding the situation at hand. This choice impacts significantly results reliability. In this paper, Hidden Markov Models (HMMs) are used to build intentional process models (Maps) from activity logs. Since HMMs parameters require to be learned, the main contribution of this paper is to compa...
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: International Journal of Computer Applications
سال: 2013
ISSN: 0975-8887
DOI: 10.5120/12876-9901