This paper introduces a two-level learning algorithm which combines parallel genetic algorithm (PGA) and backpropagation algorithm (BP) in order to evolve optimal subsets of discriminatory features for robust pattern ...
详细信息
ISBN:
(纸本)7800033813
This paper introduces a two-level learning algorithm which combines parallel genetic algorithm (PGA) and backpropagation algorithm (BP) in order to evolve optimal subsets of discriminatory features for robust pattern classification. In this approach, PGA is used to explore the space of all possible subsets of a large set of candidate discriminatory features. For a given subset, BP is invoked to be trained according to related training data. The individuals of population are evaluated by the classification performance of the trained BP according to the testing data. This process iterates until a satisfactory subset is attained. We use the classification of handwritten numeral and structure of ionosphere for experiment. The results show that this multistrategy methodology improves the classification accuracy rate and the speed of training.
The Robot Teleoperation System (RTS) based on telepresence, which is aided financially by the National 863 High-Tech Development Plan, was set up by the State Key laboratory of Intelligence technology and systems. Thi...
详细信息
The Robot Teleoperation System (RTS) based on telepresence, which is aided financially by the National 863 High-Tech Development Plan, was set up by the State Key laboratory of Intelligence technology and systems. This system consists of three main parts: robot control, stereo vision and hand gesture tracking. The controlled robot and the operator form a closed loop, and the operator views the robot's status and the environment through the stereo vision subsystem. RTS is developed from SAROT (an intelligent assembly robot system), which is logically divided into 5 layers: real time control, monitoring and coordination, motion planning, task scheduling and task planning. The paper proposes several active "path smoothing" schemes implemented in the system, which carry out the operator's hand gesture tracking in 7 DOF (position: 3, orientation: 3, and pitch: 1).
Learning accurate Bayesian network (BN) classifiers from complete databases is a very active research topic in data mining and machine learning. However, in practice, databases are rarely complete. This affects their ...
详细信息
ISBN:
(纸本)0780374908
Learning accurate Bayesian network (BN) classifiers from complete databases is a very active research topic in data mining and machine learning. However, in practice, databases are rarely complete. This affects their real world data mining applications. This paper investigates the methods for learning four types well-known Bayesian network classifiers from incomplete databases. These four types BN classifiers are: Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes, and general BN, where the latter two are learned using dependency analysis based algorithms that work only on the database completeness assumption. In order to enable this kind of algorithms to handle with missing data, this paper introduces a novel deterministic method to estimate the (conditional) mutual information from incomplete databases, which can be used to do CI tests, a fundamental step in the dependency analysis based algorithms. The experimental results show that our algorithm is efficient and reliable.
暂无评论