maths learning is seriously blocked in the phase of theinteraction of old and new knowledge,and the main factors ofthe block are lower ability of maths sumrnary,lack of interestahd pfopcr learning methods.
The theory is elaborated in following seven aspects: constructing basic theory and concept in teaching selecting instructional contents selecting instructional methods selecting learning methods selecting organization forms in teaching selecting instructional evaluation in classroom instruction designing instructional environment. In the end it forms instructional framework: theory base are constructivism and humanism; instructional method are task-oriented and curriculum integration;
For the five subscales (study method subscale, study habit subscale, study attitude subscale, study environment subscale and adaptation of body and mental subscale), the overall fitness was good, but the fitness of inner structure was not good.
The present paper argues that the moral education is supposed to be focused on the manner,emotion,and achievement in learning of the students,as well as their non-intelligent elements and study method in order to foster the development of personality of the students.
Linearization learning method of BP neural networks
This paper proposes a learning method linearizing non-linearity of the activation function and discusses its merits and demerits theoretically.
We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA+WV (Multi-Objective Evolutionary Algorithm+Weighted Voting).
Also, a new cooperative learning method called weighted strategy sharing (WSS) is introduced.
The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail.
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.