researchers of language teaching believe that teaching method has a lot of impact on the speed and depth of learning. the importance of this is so obvious on teaching some persian language structures which are syntactically and semantically complicated. one of these structures is conditional sentences which has been considered in this study. here we tried to teach conditional sentences via both...

An alternative account of human concept learning based on an invariance measure of the categorical stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or size. To do this we introduce a mathematical framework based on the notion of a Boolean dif...

We introduce DRAIL, a new declarative framework for specifying Deep Relational Models. Our framework separates structural considerations, which express domain knowledge, from the learning architecture to simplify the process of building complex structural models. We show the DRAIL formulation of two NLP tasks, Twitter Part-of-Speech tagging and Entity-Relation extraction. We compare the perform...

With the rapid development of mobile communication and computer network technologies, the new mobile learning mode has been accepted by more and more people. In this paper, taking the learning system under mobile environment and from the perspective of necessary theoretic knowledge and relative technologies for mobile learning, the structural frame of the mobile learning system was discussed an...

In this paper, we propose a recursive method for structural learning of directed acyclic graphs (DAGs), in which a problem of structural learning for a large DAG is first decomposed into two problems of structural learning for two small vertex subsets, each of which is then decomposed recursively into two problems of smaller subsets until none subset can be decomposed further. In our approach, ...

A structural similarity kernel is presented in this paper for SVM learning, especially for learning with imbalanced datasets. Kernels in SVM are usually pairwise, comparing the similarity of two examples only using their feature vectors. By building a neighborhood graph (kNN graph) using the training examples, we propose to utilize the similarity of linking structures of two nodes as an additio...