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In addition, statistical learning methods, not yet applied in particle physics, are presented and some new applications are suggested.
      
Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues.
      
The discretization method presented in this paper can also be used as a data pretreating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.
      
Application of bayesian network learning methods to land resource evaluation
      
Through intensive simulations on a truss construction task, we found that our reinforcement learning methods have great potential to contribute towards fail-safe design for multiple space robots in the above case.
      
One application would be the clustering of resulting weight vectors of an experiment in order to identify inadequate models or learning methods.
      
One of the main problems associated with artificial neural networks on-line learning methods is the estimation of model order.
      
The approach can be used to speed up several well-known learning methods such as variational Bayesian learning (ensemble learning) and expectation-maximization algorithm with modest algorithmic modifications.
      
Our work shows how ensemble learning methods could be used in an array of chemical sensors in order to deal with this problem.
      
Despite the general success of learning methods for FNN, such as the backpropagation (BP) algorithm, second-order algorithms, long learning time for convergence remains a problem to be overcome.
      
Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.
      
Since the degree prediction of malignancy is critical before brain surgery, many learning methods are used like rule induction algorithm, single neural networks, support vector machines, etc.
      
Ensemble learning methods can improve the generalization of single learning machine, and are becoming popular in the machine learning and medical data processing communities.
      
Previous studies show that redundant information can help improve the ratio of prediction accuracy between semi-supervised learning methods and supervised learning methods.
      
This paper investigates what redundant features have effect on the semi-supervised learning methods, e.g.
      
This analysis provided a very differentiated evaluation of the McMaster curriculum, demonstrating a systematic progress of learning methods from the second to the last phase.
      
Three supervised learning methods, classification tree, linear discriminant analysis and back-propagation artificial neural network, have been applied for distinguishing between classes.
      
Our conclusions were confirmed by using the machine learning methods: clustering and multiple regression.
      
In general, four-layer series-coupled machines can be divided into two types according to learning methods.
      
Using computer simulation, it is shown that dynamic competitive learning outperforms simple competitive learning methods in solving cluster detection and centroid estimation problems.
      
 

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