In this manuscript, we consider the problem of kernel classification under the Gaussian data design, and under source and capacity assumptions on the dataset. While the decay rates of the prediction error have been extensively studied under much more generic assumptions for kernel ridge regression, deriving decay rates for the classification problem has been hitherto considered a much more challenging task. In this work we leverage recent analytical results for learning curves of linear classification with generic loss function to derive the rates of decay of the misclassification (prediction) error with the sample complexity for two standard classification settings, namely margin-maximizing Support Vector Machines (SVM) and ridge classification. Using numerical and analytical arguments, we derive the error rates as a function of the source and capacity coefficients, and contrast the two methods.

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems. This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models. While you may not be familiar with SVR, chances are you have previously heard about Support Vector Machines (SVM). SVMs are most frequently used for solving classification problems, which fall under the supervised machine learning category. These use cases utilize the same idea behind support vectors, but each has a slightly different implementation.

Weighted twin support vector machines (WLTSVM) mines as much potential similarity information in samples as possible to improve the common short-coming of non-parallel plane classifiers. Compared with twin support vector machines (TWSVM), it reduces the time complexity by deleting the superfluous constraints using the inter-class K-Nearest Neighbor (KNN). Multi-view learning (MVL) is a newly developing direction of machine learning, which focuses on learning acquiring information from the data indicated by multiple feature sets. In this paper, we propose multi-view learning with privileged weighted twin support vector machines (MPWTSVM). It not only inherits the advantages of WLTSVM but also has its characteristics. Firstly, it enhances generalization ability by mining intra-class information from the same perspective. Secondly, it reduces the redundancy constraints with the help of inter-class information, thus improving the running speed. Most importantly, it can follow both the consensus and the complementarity principle simultaneously as a multi-view classification model. The consensus principle is realized by minimizing the coupling items of the two views in the original objective function. The complementary principle is achieved by establishing privileged information paradigms and MVL. A standard quadratic programming solver is used to solve the problem. Compared with multi-view classification models such as SVM-2K, MVTSVM, MCPK, and PSVM-2V, our model has better accuracy and classification efficiency. Experimental results on 45 binary data sets prove the effectiveness of our method.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensemble...

The support vector machine (SVM), developed by the computer science community in the 1990s, is a supervised learning algorithm commonly used and originally intended for a binary classification setting. It is often considered one of the best "out of the box" classifiers. The SVM is a generalization of the simple yet elegant algorithm called the maximal margin classifier. This classifier, however, cannot be applied in every situation since it relies heavily on the assumption that the dataset is linearly separable -- thus, several extensions exist. Note: In the following, we will only cover the maximal margin classifier, purposely avoiding the different extensions.

Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible use and better allocation of resources; especially in the medical industry. Activity recognition and classification can be obtained using many sophisticated data recording setups, but there is also a need in observing how performance varies among models that are strictly limited to using sensor data from easily accessible devices: smartphones and smartwatches. This paper presents the findings of different models that are limited to train using such sensors. The models are trained using either the k-Nearest Neighbor, Support Vector Machine, or Random Forest classifier algorithms. Performance and evaluations are done by comparing various model performances using different combinations of mobile sensors and how they affect recognitive performances of models. Results show promise for models trained strictly using limited sensor data collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.

Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the biosensor. In this work, we report a versatile, capture agent free biosensor platform based on an array of porous silicon (PSi) thin films, which has the potential to robustly detect a wide variety of analytes based on their physical and chemical properties in the nanoscale porous media. The ability of this system to reproducibly classify, quantify, and discriminate three proteins is demonstrated to concentrations down to at least 0.02g/L (between 300nM and 450nM) by utilizing PSi array elements with a unique combination of pore size and buffer pH, employing linear discriminant analysis for dimensionality reduction, and using support vector machines as a classifier. This approach represents a significant step towards a low cost, simple and robust biosensor platform that is able to detect a vast range of biomolecules.

Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Na\"ive Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression (SVR) as the first hidden layer and reformulates the neural network as a latent variable model. The former maps the input vector into an infinite dimensional feature space via a radial basis function (RBF) kernel, ensuring absence of local minima on its training loss surface. The latter breaks the high-dimensional nonconvex neural network training problem into a series of low-dimensional convex optimization problems, and enables its prediction uncertainty easily assessed. The K-StoNet can be easily trained using the imputation-regularized optimization (IRO) algorithm. Compared to traditional deep neural networks, K-StoNet possesses a theoretical guarantee to asymptotically converge to the global optimum and enables the prediction uncertainty easily assessed. The performances of the new model in training, prediction and uncertainty quantification are illustrated by simulated and real data examples.

The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the signatures (Raman shift values) are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.