Recent research about margin theory has proved that maximizing the minimum margin like support vector machines does not necessarily lead to better performance, and instead, it is crucial to optimize the margin distribution. In the meantime, margin theory has been used to explain the empirical success of deep network in recent studies. In this paper, we present mdNet (the Optimal Margin Distribution Network), a network which embeds a loss function in regard to the optimal margin distribution. We give a theoretical analysis of our method using the PAC-Bayesian framework, which confirms the significance of the margin distribution for classification within the framework of deep networks. In addition, empirical results show that the mdNet model always outperforms the baseline cross-entropy loss model consistently across different regularization situations. And our mdNet model also outperforms the cross-entropy loss (Xent), hinge loss and soft hinge loss model in generalization task through limited training data.

We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, kernelized LASSO and $\varepsilon$-SVR.

This article proposes a two-dimensional classification methodology to select the relevant forecasting tools developed by the scientific community based on a classification of load forecasting studies. The inputs of the classifier are the articles of the literature and the outputs are articles classified into categories. The classification process relies on two couple of parameters that defines a forecasting problem. The temporal couple is the forecasting horizon and the forecasting resolution. The system couple is the system size and the load resolution. Each article is classified with key information about the dataset used and the forecasting tools implemented: the forecasting techniques (probabilistic or deterministic) and methodologies, the cleansing data techniques and the error metrics. This process is illustrated by reviewing and classifying thirty-four articles.

Emotional content is a crucial ingredient in user-generated videos. However, the sparsely expressed emotions in the user-generated video cause difficulties to emotions analysis in videos. In this paper, we propose a new neural approach---Bi-stream Emotion Attribution-Classification Network (BEAC-Net) to solve three related emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization, in an integrated framework. BEAC-Net has two major constituents, an attribution network and a classification network. The attribution network extracts the main emotional segment that classification should focus on in order to mitigate the sparsity problem. The classification network utilizes both the extracted segment and the original video in a bi-stream architecture. We contribute a new dataset for the emotion attribution task with human-annotated ground-truth labels for emotion segments. Experiments on two video datasets demonstrate superior performance of the proposed framework and the complementary nature of the dual classification streams.

Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused on this kind of problems. Nonparallel Support Vector Ordinal Regression (NPSVOR) is a SVM-based OR model, which learns a hyperplane for each rank by solving a series of independent sub-optimization problems and then ensembles those learned hyperplanes to predict. The previous studies are focused on its nonlinear case and got a competitive testing performance, but its training is time consuming, particularly for large-scale data. In this paper, we consider NPSVOR's linear case and design an efficient training method based on the dual coordinate descent method (DCD). To utilize the order information among labels in prediction, a new prediction function is also proposed. Extensive contrast experiments on the text OR datasets indicate that the carefully implemented DCD is very suitable for training large data.

Abstract--Vocal disorders have affected several patients all over the world. Due to the inherent difficulty of diagnosing vocal disorders without sophisticated equipment and trained personnel, a number of patients remain undiagnosed. To alleviate the monetary cost of diagnosis, there has been a recent growth in the use of data analysis to accurately detect and diagnose individuals for a fraction of the cost. We propose a cheap, efficient and accurate model to diagnose whether a patient suffers from one of three vocal disorders on the FEMH 2018 challenge. I. INTRODUCTION The human standard of life can be severely affected by their individual pathological voice condition. This has also financially burdened several patients, organizations, and societies [1].Some of the common impairments to the voice are structural lesions, neoplasms, and neurogenic disorders [1].

In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is one of the major algorithms for intrusion detection. In this research, we studied a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction.Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. The network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. Steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation.The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm.Also the KNN algorithm has been compared in situations with and without diminishing dimensions,the results have shown that the proposed method has a better performance than comparable methods.

Differential equations are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. Although there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the Theory of Connections (ToC) with one based on Support Vector Machines (SVM). The ToC method uses a constrained expression (an expression that always satisfies the differential equation constraints), which transforms the process of solving a differential equation into an unconstrained problem, and is ultimately solved via least-squares. In addition to individual analysis, the two methods are merged into a new methodology, called constrained SMVs (CSVM), by incorporating the SVM method into the ToC framework to solve unconstrained problems. Numerical tests are conducted on three sample problems: one first order linear ODEs, one first order non-linear ODE, and one second order linear ODE. Using the SVM method as a benchmark, a speed comparison is made for all the problems by timing the training period, and an accuracy comparison is made using the maximum error and mean-squared error on the training and test sets. In general, ToC is shown to be slightly faster (by an order of magnitude or less) and more accurate (by multiple orders of magnitude) over the SVM and CSVM approaches.

Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is usually from sensor arrays, which results in data which are highly redundant and correlated, an area in which traditional two-way matrix approaches have had difficulty in deconstructing and interpreting. Newer methods involving tensor analysis allow us to analyse this multi-way structural data in a coherent manner. In our approach, we demonstrate the usefulness of tensor-based learning coupled with for damage detection, on a novel $N$-DoF Lagrangian aeroservoelastic model.

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other plane-based clustering methods. The non-convex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.