Assessment of visual quality plays a crucial role in modeling, implementation, and optimization of imageand video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extrac...

Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches for vocal fold pathology diagnosis. These algorithms usually have two stages which are Feature Extraction and Classification. While the second stage implies a choice of a var...

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the k-fold subsampling and sequential ba...

Speech analysis applications are typically based on short-term spectral analysis of the speech signal. Feature extraction process outputs one feature vector per frame. The features are further processed by application-dependent techniques, such as hidden Markov models or vector quantization. Independent from the application, it is often desirable that the feature vectors form separable clusters...

The explosive amount of malware continues their threats in network and operating systems. Signature-based method is widely used for detecting malware. Unfortunately, it is unable to determine variant malware on-the-fly. On the hand, behavior-based method can effectively characterize the behaviors of malware. However, it is time-consuming to train and predict for each specific family of malware....

We address a situation when more than one feature subset allows for linear separability of given data sets. Such situation can occur if a small number of cases is represented in a highly dimensional feature space. The method of the feature selection based on minimisation of a special criterion function is here analysed. This criterion function is convex and piecewise-linear (CPL). The proposed ...

we perform three sets of experiments. From the first experiment, the systems are trained using all the 41 features. The second experiment where we perform feature selection by using Gain Ratio as to select the best features instead of using all the 41 features and perform the experiment with Linear SVM, SGD and Adaptive Boost and compare the results. The third experiment where we perform featur...

In this work, event related potentials (ERPs) induced by visual stimuli categorized with different value of affective valence are studied. EEG signals are recorded during visualization of selected pictures belonging to International Affective Picture System (IAPS). A Morlet wavelet filter is used to transform the EEG input space to a topography-time–frequency feature space. Support vector machi...

Speech analysis applications are typically based on short-term spectral analysis of the speech signal. Feature extraction process outputs one feature vector per frame. The features are further processed by application-dependent techniques, such as hidden Markov models or vector quantization. Independent from the application, it is often desirable that the feature vectors form separable clusters...

Searching the feature space for a subset yielding optimum performance tends to be expensive, especially in applications where the cardinality of the feature space is high (e.g., text categorization). This is particularly true for massive datasets and learning algorithms with worse than linear scaling factors. Linear Support Vector Machines (SVMs) are among the top performers in the text classif...