They provide maximum-likelihood based learning method to learn influence parameters in these topic-aware models.
Support vector machine (SVM) based learning methods are shown effective for predictions of protein subcellular and subnuclear localizations.
Anchorgraph based learning methods have met with success in modeling the large data for scalable semi-supervised learning.
K-PLS and K-SVM solutions in non-linear, non-separable learning environments utilize kernel based learning methods.
This may have interfered in the students' interest in the computer game or even in their attention span.In general, studies that have presented good results for computer game-based learning methods refer to the use of one computer per student [ 27, 37, 38, 41, 48].
The first approach is to compute some distance or similarity scores between genes based on the GO information [e.g. following the approach by Srivastava et al. (2008)] and then estimate the network topology based on a network learning method, for example, graphical lasso (Friedman et al., 2008).
In addition, an experiment was conducted to obtain the performance of our proposed fusion-based learning method for the collected dataset.
This paper describes a case-based learning method for acquiring context-dependent user optimization preferences and tradeoffs and using them to incrementally improve schedule quality in predictive scheduling and reactive schedule management in response to unexpected execution events.
In this paper, we propose a highly efficient learning method for MI classification based on hierarchical extreme learning machine (ELM), called MI-ELM.
Kernel-based learning methods such as SVMs showed high sensitivity and specificity and were employed by a majority of research groups for classifying prostate cancer from normal tissue.
Most of the reinforcement learning methods for robotics were based on state information like joint states of robot arms.
