Managing customer churn is one major challenge facing companies, especially those that offer subscription-based services. Customer churn (aka customer attrition) can be defined as the loss of customers, and it is caused by a change in taste, lack of proper customer relationship strategy, change of residence and several other reasons. In this article, I will employ the superpowers of machine learning to assist a hypothetical company in predicting customer churn. If businesses can effectively predict customer attrition, they can segment those customers that are highly likely to churn and provide better services to them. In this way, they can achieve a high customer retention rate and maximize their revenue.

Abstract--It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-theart deeplearning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.

Recessions reflect great dislocation in the economy and are often the source of societal anxiety. During a recession, unemployment is usually higher, and output is lower. Accurately identifying turning points from expansions to recessions has broad use for policymakers, business executives, academics, and individuals. Additionally, investors with enough resources to use this information in their investment process may change their portfolios as the economy turns from growth to contraction. There have been several attempts in the literature to accurately predict the timing of recessions.

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

Manifold regularization, such as laplacian regularized least squares (LapRLS) and laplacian support vector machine (LapSVM), has been widely used in semi-supervised learning, and its performance greatly depends on the choice of some hyper-parameters. Cross-validation (CV) is the most popular approach for selecting the optimal hyper-parameters, but it has high complexity due to multiple times of learner training. In this paper, we provide a method to approximate the CV for manifold regularization based on a notion of robust statistics, called Bouligand influence function (BIF). We first provide a strategy for approximating the CV via the Taylor expansion of BIF. Then, we show how to calculate the BIF for general loss function,and further give the approximate CV criteria for model selection in manifold regularization. The proposed approximate CV for manifold regularization requires training only once, hence can significantly improve the efficiency of traditional CV. Experimental results show that our approximate CV has no statistical discrepancy with the original one, but much smaller time cost.

The main goal of statistical learning theory is to provide a fundamental framework for the problem of decision making and model construction based on sets of data. Here, we present a brief introduction to the fundamentals of statistical learning theory, in particular the difference between empirical and structural risk minimization, including one of its most prominent implementations, i.e. the Support Vector Machine.

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.

We present a quantum machine learning algorithm for training Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the $L_1$ norm of the feature weights vector. Sparse SVM results in a classifier that uses only a small fraction of the input features in making decisions, and is especially suitable for cases where the total number of features is at the same order, or larger, than the number of training samples. The algorithm utilizes recently proposed quantum solvers for semidefinite programming and linear programming problems. We show that while for an arbitrary binary classification problem no quantum speedup is achieved by using quantum SDP/LP solvers during training, there are realistic scenarios in which using a sparse linear classifier makes sense in terms of the expected accuracy of predictions, and polynomial quantum speedup compared to classical methods can be achieved.

Voice activity detection (VAD), used as the front end of speech enhancement, speech and speaker recognition algorithms, determines the overall accuracy and efficiency of the algorithms. Therefore, a VAD with low complexity and high accuracy is highly desirable for speech processing applications. In this paper, we propose a novel training method on large dataset for supervised learning-based VAD system using support vector machine (SVM). Despite of high classification accuracy of support vector machines (SVM), trivial SVM is not suitable for classification of large data sets needed for a good VAD system because of high training complexity. To overcome this problem, a novel ensemble-based approach using SVM has been proposed in this paper.The performance of the proposed ensemble structure has been compared with a feedforward neural network (NN). Although NN performs better than single SVM-based VAD trained on a small portion of the training data, ensemble SVM gives accuracy comparable to neural network-based VAD. Ensemble SVM and NN give 88.74% and 86.28% accuracy respectively whereas the stand-alone SVM shows 57.05% accuracy on average on the test dataset.

In the present paper, a method of defining the industrial process parameters for a new product using machine learning algorithms will be presented. The study will describe how to go from the product characteristics till the prediction of the suitable machine parameters to produce a good quality of this product, and this is based on an historical training dataset of similar products with their respective process parameters. In the first part of our study, we will focus on the ultrasonic welding process definition, welding parameters and on how it operate. While in second part, we present the design and implementation of the prediction models such multiple linear regression, support vector regression, and we compare them to an artificial neural networks algorithm. In the following part, we present a new application of Convolutional Neural Networks (CNN) to the industrial process parameters prediction. In addition, we will propose the generalization approach of our CNN to any prediction problem of industrial process parameters. Finally the results of the four methods will be interpreted and discussed.