Transfer Learning Research Based On Knowledge Representation

Along with the rapid development of computer information technology, to mineuseful information from mass data and put them into use have become a currentresearch hot spot. In data mining, the assumption for the traditional machine learningis that training data and test data have the same distribution. While in the practicalapplication, this assumption cannot be often met. To meet this assumption barely, theefficiency of data analysis has to be reduced. With the use of different fields ofknowledge for target task learning, transfer learning can transfer and share theinformation between similar domains or tasks, making the traditional learning fromscratch an addable one, which implies that the learning efficiency is higher and thecost is lower, so that it has become the focus in the field of data mining and machinelearning in recent years.The most remarkable characteristic of transfer learning is that it can employ theknowledge in relative domains to help perform the learning tasks in the domain of thetarget. However, different ways of knowledge expression directly affect the effect oftransfer learning. Beginning with the ways of knowledge expression, aiming at thecombination modes of knowledge expression and transfer methods, this paper unfoldsits study mainly as follows:Firstly, specific to the situation that the shared knowledge in the domains of thesource and the target are sample data with similar distribution, an instance transferlearning method based on multi-sources dynamic TrAdaBoost is put forward.Integrated with the knowledge in multiple source domains, this method makes thetarget task learning the one that is able to make good use of the information of allsource domains. Whenever candidate classifiers are trained, all the samples in allsource domains are involved in learning, and the information conducive to target tasklearning can be obtained, so that negative transfer can be avoided. The theoreticalanalysis suggests that the given algorithm better than single source transfer, by meansof adding the dynamic factor, this algorithm improves the defect that weight entropyto drift from source to target instances. The experimental results support that the givenalgorithm has the advantage of improving the recognition rate and classificationaccuracy.Secondly, specific to the situation that shared knowledge in the domains of thesource and the target are sample data with different distribution, a feature transfer learning based on covariance matrix is proposed. This method employs similarlearning to estimate the covariance pairs of individual parameters. Starting from thecharacteristic relation between data, by means of constructing semidefinite program,the estimated values are combined, the priori values of the current tasks are emulated,and the priori multivariate gaussian covariance matrix is automatically built in order toeffectively predict the unlabelled data. The experimental results show that, with theuse of a small amount of the source tasks, good transfer learning results can beachieved by the given method.Thirdly, specific to the situation that shared knowledge are the parametric modelsor the priori distributions of some base functions, a parameter transfer learningmethod based on hierarchical bayes is brought forward. With the use of this method,the hierarchy concept of task dependency is defined, the standard Dirichlet processmodel is developed, and then the bayes learning model is built under multi-taskenvironment. The number of categories, the inference process of types and thecategory structure used for exploring a new task are automatically studied, thus moreknowledge are learned quickly. The experimental results show that the proposedmethod can speed up the rate of convergence of the optimal strategy in the newdomain.Finally, specific to the situation that shared knowledge are associated knowledgecontaining rules, structure and logic, an rule-based transfer learning methodassociated with Markov logic network is presented. Having applied this method, bymeans of pseudo log-likelihood function, the knowledge in the source domainexpressed in Markov logic network are transferred into the target domain while thelink between the two domains is established. By means of the self diagnosis andstructure update in the source domain and the new clause surf in the target domain,the mapped structure is optimized so that it can be adapt to the learning in the targetdomain. The experimental results show that the given algorithm successfully maps thetransferred knowledge, and improves the precision of the learning model.Aiming at some existing problems in the current study on transfer learning, withhow to utilize the different knowledge expressions shared in both source and targetdomains as the starting point, with improving the learning efficiency in the targetdomain as the purpose, with different transfer methods as the basic means, with anin-depth study on how to construct the transfer environment and how to build theeffective transfer model under the transfer environment, some solutions are given in the full text. The related simulation results have verified the feasibility and availabilityof the given solutions. The research results in this paper have enriched transferlearning theories, which provide a helpful guidance for complex data mining.