The Effect of Think Pair Share (TPS) Learning Model With Problem Solving Approach on the Student's Math Communication in MA DA Jarowaru "

The purpose of this study was to determine the effect of Think Pair Share (TPS) learning models with problem solving approaches. The approach used in this research is a qualitative approach with the type of experimental research "The effect of Think Pair Share (TPS) learning model with problem solving approach to students' mathematical communication in MA DA Jerowaru. Model Pair Pair Share (TPS), as the name "Thinking" learning begins with the teacher asking questions or issues related to the lesson for students to think about, "pairing", at this stage the teacher asks students to pair up pairs. Give the couple a chance to discuss. It is hoped that this discussion will be able to deepen the meaning of the answers they have thought through intersubjectives with their partners. The results of intersubjective discussions in each pair of results are discussed with the whole class pair. This stage is known as "sharing" in this activity. It is expected that question and answer will occur which encourages the integrative management of knowledge. Added to this is the emphasis on problem solving, namely the Problem Solving approach. Problem Solving is an approach that teaches students how to solve a problem. Meanwhile, according to Heriawan (in Istiqoma and Rusdi, 2012: 92). Problem Solving is a way of presenting learning material by making problems as a starting point for discussion to be analyzed in an effort to find solutions or answers by students.

Adaptive classification to reduce non-stationarity in visual evoked potential brain-computer interfaces

Non-stationarity of electroencephalogram (EEG) signals greatly affect classifier performance in brain-computer interface (BCI). To overcome this problem we propose an adaptive classifier model known as extended multi-class pooled mean linear discriminant analysis (EMPMLDA). Here, we update the average class pair co-variance matrix along with pooled mean values. Evaluation of classifiers are done on visual evoked cortical potential data-sets. We demonstrate that EMPMLDA can significantly outperform other static classifiers such as MLDA and adaptive classifiers (MPMLDA). Furthermore an optimal update coefficient can be achieved using different datasets.

CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs) from multispectral image (MSI) and light detection and ranging (LiDAR) data. In particular, the <q>one-against-one</q> strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA) in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

On Jacobson Near-rings and Special Radicals

In this paper, we construct special radicals using class pairs of near-rings. We establish necessary conditions for a class pair to be a special radical class. We then define Jacobson-type near-rings and show that in most cases the class of all near-rings of this type is a special radical class. Subsequently, we investigate the relationship between each Jacobson-type near-ring and the corresponding matrix near-ring.