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Much attention is focused on the study of flipped classroom. This research is conducted among top students with the purpose of confirming the effect of flipped classroom. It found that flipped classroom does not merely implies learning by oneself, even if objects are top students, teachers are expected...

AdaBoost is an ensemble method to construct a strong classifier with linear combination of base classifiers, which has been applied to relevance vector machine (RVM) for performance improvement. However, the combination of the RVM and AdaBoost can be overfitting in dealing with the noisy data sets because...

Relevance vector machine (RVM), a sparse Bayesian kernel method in machine learning, has been well-known for its sparsity and probabilistic predictions. Like other kernel methods, it use the kernel functions to map the input instances into higher dimensional space for problem simplicity. At present,...

The significant jump in world crude oil price over the past year has raised great concern over the economic impact that such a price shock may bring about on Taiwan’s economy, which has been characterized by extremely high import-oil dependence. Previous analyses have been tackling similar issue from...

This paper provides the most complete evidence to date on the importance of monetary aggregates as a policy tool in an inflation forecasting experiment. Every possible definition of ‘money’ in the USA is being considered for the full data period (1960 – 2006), using the most sophisticated non-linear...

The main objective of this study is developing a linguistic cluster model in order to meet the public security index requirement and extract crime rule in time series. In contrast to the current studies in crime theory which mostly rely on traditional behavior science, we turned to a hybrid approach...

This paper presents a novel approach to natural scene segmentation. It uses both color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on homogeneity, is proposed. Natural scene segmentation...

In this paper we present a novel dense matching algorithm that relies on sparse stereo data in order to build a dense disparity map. The algorithm uses a recursive updating scheme to estimate the dense stereo data using various interpolation techniques. The major problem of classical template matching...

The self-organizing map (SOM), as a kind of unsupervised neural network, has been applied for both static data management and dynamic data analysis. To further exploit its ability in search, in this paper, we propose an SOM-based search algorithm (SOMS) for dynamic systems, in which the SOM is employed...

In this paper, a progressive road analyzes using fuzzy technique is proposed. In this system, only the low cost GPS receivers are used. The road attribute and map are constructed only using the received data.

In this paper, we issue a hybrid system, which is integrated with the fuzzy system and Petri net models. We exploit the characteristics, imprecise or ambiguous information, of fuzzy theory to map the algorithm of crisp inputs and outputs, furthermore, model problems, product a nonlinear function, and...