User profiles for Han-Tai Shiao
 | Han-Tai ShiaoVanguard Verified email at vanguard.com Cited by 246 |
SVM-based system for prediction of epileptic seizures from iEEG signal
HT Shiao, V Cherkassky, J Lee, B Veber… - IEEE Transactions …, 2016 - ieeexplore.ieee.org
Objective: This paper describes a data-analytic modeling approach for the prediction of epileptic
seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even …
seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even …
[HTML][HTML] Online prediction of lead seizures from iEEG data
We describe a novel system for online prediction of lead seizures from long-term intracranial
electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This …
electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This …
Through the azerothian looking glass: Mapping in-game preferences to real world demographics
Examining how in-game behavior preferences map onto real world demographics provides
important empirically-derived insights into how to match game-based mechanisms to target …
important empirically-derived insights into how to match game-based mechanisms to target …
Investor behavior modeling by analyzing financial advisor notes: a machine learning perspective
Modeling investor behavior is crucial to identifying behavioral coaching opportunities for
financial advisors. With the help of natural language processing (NLP) we analyze an …
financial advisors. With the help of natural language processing (NLP) we analyze an …
Learning using privileged information (LUPI) for modeling survival data
HT Shiao, V Cherkassky - 2014 International Joint Conference …, 2014 - ieeexplore.ieee.org
Survival data is common in medical applications. The challenge in applying predictive data-analytic
methods to survival data is in the treatment of censored observations, since the …
methods to survival data is in the treatment of censored observations, since the …
[PDF][PDF] SVM-based approaches for predictive modeling of survival data
HT Shiao, V Cherkassky - … of the International Conference on Data …, 2013 - world-comp.org
Survival data is common in medical applications. The challenge in applying predictive data-analytic
methods to survival data is in the treatment of censored observations. The survival …
methods to survival data is in the treatment of censored observations. The survival …
Implementation and comparison of SVM-based multi-task learning methods
HT Shiao, V Cherkassky - The 2012 International Joint …, 2012 - ieeexplore.ieee.org
Exploiting additional information to improve traditional inductive learning is an active research
area in machine learning. In many supervised-learning applications, data can be naturally …
area in machine learning. In many supervised-learning applications, data can be naturally …
Using Machine Learning to Model Advised-Investor Behavior.
During periods of extreme market volatility, such as that experienced during the COVID-19
pandemic, advised investors may consider impulsive and inappropriate investment decisions …
pandemic, advised investors may consider impulsive and inappropriate investment decisions …
Reliable seizure prediction from EEG data
V Cherkassky, B Veber, J Lee, HT Shiao… - … Joint Conference on …, 2015 - ieeexplore.ieee.org
There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic
seizures from EEG recording of brain activity [1-10]. Even though there is clear evidence …
seizures from EEG recording of brain activity [1-10]. Even though there is clear evidence …
Group learning for high-dimensional sparse data
We describe new methodology for supervised learning with sparse data, ie, when the
number of input features is (much) larger than the number of training samples (n). Under the …
number of input features is (much) larger than the number of training samples (n). Under the …