Evaluating human brain potentials during watching different images can be used for memory evaluation, information retrieving, guilty-innocent identification and examining the brain response. In this study, the effects of watching images, with different levels of familiarity, on subjects' Electroencephalogram (EEG) have been studied. Three different groups of images with three familiarity levels of "unfamiliar", "familiar" and "very familiar" have been considered for this study. EEG signals of 21 subjects (14 men) were recorded. After signal acquisition, pre-processing, including noise and artifact removal, were performed on epochs of data. Features, including spatial-statistical, wavelet, frequency and harmonic parameters, and also correlation between recording channels, were extracted from the data. Then, we evaluated the efficiency of the extracted features by using p-value and also an orthogonal feature selection method (combination of Gram-Schmitt method and Fisher discriminant ratio) for feature dimensional reduction. As the final step of feature selection, we used 'add-r take-away l' method for choosing the most discriminative features. For data classification, including all two-class and three-class cases, we applied Support Vector Machine (SVM) on the extracted features. The correct classification rates (CCR) for "unfamiliar-familiar", "unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%, 92.6%, and 70.6%, respectively. The best results of classifications were obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency and harmonic features were among the most discriminative features. Finally, in three-class case, the best CCR was 86.8%.

Welcome to the second stepping stone of Supervised Machine Learning. Again, this chapter is divided into two parts. Part 2 (here) we take on small coding exercise challenge. If you haven't read the Naive Bayes, I would suggest you to read it thorough here. Don't worry, we shall learn in laymen terms.

Development of black-box modeling techniques, like support vector machine (SVM), neural networks, etc., has shown rather rapid in the past decades (Yuan et al., 2016; Zhao et al., 2015; Wu et al., 2013). This sort of techniques, compared to white-box modeling methods (also called mechanism-based modeling or first-principles modeling), works without any need of knowing the internal structure or details on variables interaction in systems considered, so they are suited to describe extremely complex objectives, such as human brain (Khosrowabadi et al., 2014), black hole (Grumiller et al., 2012), integrated industrial processes (Gao et al., 2012) and so on. Essentially, blackbox modeling is an input-output data-based approach, and the model precision mainly depends on data quality, model structure and parameters identification algorithm. In order to develop high-precision black-box models, it always needs reliable and representative data, smart mathematical treatment and efficient identification algorithms. All of these are challenging the development of the black-box modeling techniques.

This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some loss functions such as the truncated quadratic loss and quadratic loss, SVM with Gaussian kernel can reach the almost optimal learning rate, provided the regression function is smooth. Our second result shows that, for a large number of loss functions, under some Tsybakov noise assumption, if the regression function is infinitely smooth, then SVM with Gaussian kernel can achieve the learning rate of order $m^{-1}$, where $m$ is the number of samples.

Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of a novel approach for nonparametric probabilistic forecasting of wind power that combines a smooth approximation of the pinball loss function with a neural network architecture and a weighting initialization scheme to prevent the quantile cross over problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

This example assumes basic familiarity with scikit-learn. Search for parameters of machine learning models that result in best cross-validation performance is necessary in almost all practical cases to get a model with best generalization estimate. A standard approach in scikit-learn is using GridSearchCV class, which takes a set of values for every parameter to try, and simply enumerates all combinations of parameter values. The complexity of such search grows exponentially with addition of new parameters. A more scalable approach is using RandomizedSearchCV, which however does not take advantage of the structure of a search space.

This supervised machine learning algorithm has strong regularization and can be leveraged both for classification or regression challenges. They are characterized by usage of kernels, the sparseness of the solution and the capacity control gained by acting on the margin, or on number of support vectors, etc. The capacity of the system is controlled by parameters that do not depend on the dimensionality of feature space. Since the SVM algorithm operates natively on numeric attributes, it uses a z-score normalization on numeric attributes. In regression, Support Vector Machines algorithms use epsilon-insensitivity (margin of tolerance) loss function to solve regression problems.

Weeks ago my Twitter timeline was full of negative stories. My timeline was so full of negative content that I started thinking Twitter was full of hate:_( That's why I decided to get some metrics to see if my intuition was right. With the help of Mª Asunción Jiménez Cordero, a PhD student specialized in Machine Learning, we developed a Spark application written in Scala to analyze real-time tweets and classify them as negative, positive or neutral using Support Vector Machine. In this post, we'll tell you how we developed this app in order to get the results you can find at https://pedrovgs.github.io/Roma/. Let's split this post in the different challenges we faced!

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.