In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estimator (SURE). We define overfitting, underfitting, and generalization using the obtained true and generalization errors. We introduce cross validation and two well-known examples which are $K$-fold and leave-one-out cross validations. We briefly introduce generalized cross validation and then move on to regularization where we use the SURE again. We work on both $\ell_2$ and $\ell_1$ norm regularizations. Then, we show that bootstrap aggregating (bagging) reduces the variance of estimation. Boosting, specifically AdaBoost, is introduced and it is explained as both an additive model and a maximum margin model, i.e., Support Vector Machine (SVM). The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting. As examples of regularization, the theory of ridge and lasso regressions, weight decay, noise injection to input/weights, and early stopping are explained. Random forest, dropout, histogram of oriented gradients, and single shot multi-box detector are explained as examples of bagging in machine learning and computer vision. Finally, boosting tree and SVM models are mentioned as examples of boosting.

The problem of forecasting stock price movements, due to market's uncertainty from incoming news, nonlinear financial instruments and behavioral and emotional biases is a challenging task facing academics and practitioners in the field; perhaps by far more complex than predicting the course of a comet by a physicist. In the past, many models have been proposed to face this problem including Support vector regression (SVR) as the extended routine designed from Support Vector Machines (SVM). Originally introduced by Vapnik for classification problems, SVM was redesigned to solve regression problems in the SVR framework. Nevertheless, SVM can solve small-sample, nonlinear and high dimension problems by using the structural risk minimization principle instead of the empirical risk principle, which could theoretically guarantee to achieve the global optimum [9]. Although SVR experimental results have shown great performance compared to other nonlinear methods [39, 40], its performance mainly depends on the choice of parameters.

Machining processes are most accurately described using complex dynamical systems that include nonlinearities, time delays and stochastic effects. Due to the nature of these models as well as the practical challenges which include time-varying parameters, the transition from numerical/analytical modeling of machining to the analysis of real cutting signals remains challenging. Some studies have focused on studying the time series of cutting processes using machine learning algorithms with the goal of identifying and predicting undesirable vibrations during machining referred to as chatter. These tools typically decompose the signal using Wavelet Packet Transforms (WPT) or Ensemble Empirical Mode Decomposition (EEMD). However, these methods require a significant overhead in identifying the feature vectors before a classifier can be trained. In this study, we present an alternative approach based on featurizing the time series of the cutting process using its topological features. We utilize support vector machine classifier combined with feature vectors derived from persistence diagrams, a tool from persistent homology, to encode distinguishing characteristics based on embedding the time series as a point cloud using Takens embedding. We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental time series from a turning cutting test. Our results show that in most cases combining the TDA-based features with a simple Support Vector Machine (SVM) yields accuracies that either exceed or are within the error bounds of their WPT and EEMD counterparts.

We propose a novel, flexible algorithm for combining together metaheuristic optimizers for non-convex optimization problems. Our approach treats the constituent optimizers as a team of complex agents that communicate information amongst each other at various intervals during the simulation process. The information produced by each individual agent can be combined in various ways via higher-level operators. In our experiments on key benchmark functions, we investigate how the performance of our algorithm varies with respect to several of its key modifiable properties. Finally, we apply our proposed algorithm to classification problems involving the optimization of support-vector machine classifiers.

In a Data Science interview, the interviewer asked me, how would you explain top data science algorithms to a non-tech person. I told him that Data science is…..(read the article to know:D). The explanation is too simple that you can easily understand. We will discuss mostly machine learning algorithms that are important for data scientists and classify them based on supervised and unsupervised roles. I will provide you an outline for all the important algorithms that you can deploy for improving your data science operations. Here is the list of top Data Science Algorithms that you must know to become a data scientist.

Support Vector Machines have been a popular topic for quite some time now, and as they develop, a need for new methods of feature selection arises. This work presents various approaches SVM feature selection developped during the author's summer internship at STMicroelectronics. The work focuses on the use of one-class SVM's for wafer testing. A key problem in OC-SVM algorithms is dimensionality reduction, otherwise known as feature selection. Prior to the execution of the proper SVM part of the algorithm, the program first needs to asses what will be the most significant data to take into account.

The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient's race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine.

Google Assistant can draw on voice command, as seen here at the Google I/O conference in 2018, with the help of machine learning techniques. Artificial intelligence systems powered by machine learning have been creating headlines with applications as varied as making restaurant reservations by phone, sorting cucumbers, and distinguishing chihuahuas from muffins. Media buzz aside, many fast-growing startups are taking advantage of machine learning (ML) techniques like neural networks and support vector machines to learn from data, make predictions, improve products, and enhance business decisions. Unfortunately "machine learning theater" – companies pretending to use the technology to make theirs seem more sophisticated for a higher valuation – is also on the rise. Undeniably, ML is transforming businesses and industries, with some more likely to benefit than others.

The quantity of labelled data is small due to privacy concerns and the cost of data acquisition and labelling by a medical expert. Furthermore, it is quite common that collected data are unbalanced and getting enough data to personalize models for individuals is very expensive or even infeasible. This paper addresses these problems by (1) designing a recurrent Generative Adversarial Network to generate realistic synthetic data and to augment the original dataset, (2) enabling the generation of balanced datasets based on heavily unbalanced dataset, and (3) to control the data generation in such a way that the generated data resembles data from specific individuals. We apply these solutions for sleep apnea detection and study in the evaluation the performance of four well-known techniques, i.e., K-Nearest Neighbour, Random Forest, Multi-Layer Perceptron, and Support Vector Machine.

We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and explain why its simple yet very efficient idea has worked in other fields of application and why its characteristics are promising for short-term power applications as well. We compare its performance with different state-of-the-art electricity price forecasting models such as quantile regression averaging (QRA) in an empirical out-of-sample study for three short-term electricity time series. We combine Conformal Prediction with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that Conformal Prediction yields sharp and reliable prediction intervals in short-term power markets. We further inspect the effect each of Conformal Prediction's model components has and provide a path-based guideline on how to find the best CP model for each market.