In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-...

The Support Vector Machine (SVM) is a powerful classification technique that has been used extensively in the field of medical imaging. A model based on SVM with Gaussian RBF kernel is proposed here for the automatic detection of brain tumor from MRI images. Various textural characteristics of the MRI images of human brain are extracted to construct a feature set. These features sets are then u...

Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted...

We propose a local boosting method in classification problems borrowing from an idea of the local likelihood method. The proposed method includes a simple device to localization for computational feasibility. We proved the Bayes risk consistency of the local boosting in the framework of PAC learning. Inspection of the proof provides a useful viewpoint for comparing the ordinary boosting and the...

With the explosive growth of music recordings, automatic classification of music emotion becomes one of the hot spots on research and engineering. Typical music emotion classification (MEC) approaches apply machine learning methods to train a classifier based on audio features. In addition to audio features, the MIDI and lyrics features of music also contain useful semantic information for pred...

In order to automatically extract ecologically useful information from audio recordings of birds, we need fast and accurate algorithms to classify bird sounds. We conduct a large-scale empirical study to evaluate algorithms for classifying bird species from audio using combinations of 3 feature sets (Mel-frequency cepstral coefficients, average spectra, and noise-robust measurements), with 10 c...

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) has become a popular research topic attracted wide attention. However, how to extract effective features from EEG signals accurately recognize them by classifiers have also an increasingly ...

iabetes is known to be one of the chronic and dangerous diseases the people in afflicted with which are increasing in number. On the other hand, early detection of this disease can be effective in treatment and control of disease progression for patients. Using the data mining techniques and machine learning, disease detection systems have been able to help patients and physicians to detect the...

SpatialBoost extends AdaBoost to incorporate spatial reasoning. We demonstrate the effectiveness of SpatialBoost on the problem of interactive image segmentation. Our application takes as input a trimap of the original image, trains SpatialBoost on the pixels of the object and the background and use the trained classifier to classify the unlabeled pixels. The spatial reasoning is introduced in ...