Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function, which has been defined in terms of the parameter $\tau$. The parameter $\tau$ can take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the same optimization problem for all values of $\tau$ in $[ -1,1]$. In this paper, we improve the existing Pin-SVM model for the binary classification task. At first, we note that there is major difficulty in Pin-SVM model (Huang et al. [1]) for $ -1 \leq \tau < 0$. Specifically, we show that the Pin-SVM model requires the solution of different optimization problem for $ -1 \leq \tau < 0$. We further propose a unified model termed as Unified Pin-SVM which results in a QPP valid for all $-1\leq \tau \leq 1$ and hence more convenient to use. The proposed Unified Pin-SVM model can obtain a significant improvement in accuracy over the existing Pin-SVM model which has also been empirically justified by extensive numerical experiments with real-world datasets.

The foundational concept of Max-Margin in machine learning is ill-posed for output spaces with more than two labels such as in structured prediction. In this paper, we show that the Max-Margin loss can only be consistent to the classification task under highly restrictive assumptions on the discrete loss measuring the error between outputs. These conditions are satisfied by distances defined in tree graphs, for which we prove consistency, thus being the first losses shown to be consistent for Max-Margin beyond the binary setting. We finally address these limitations by correcting the concept of Max-Margin and introducing the Restricted-Max-Margin, where the maximization of the loss-augmented scores is maintained, but performed over a subset of the original domain. The resulting loss is also a generalization of the binary support vector machine and it is consistent under milder conditions on the discrete loss.

The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through a phenomenon of support vector proliferation, where every training example used to fit an SVM becomes a support vector. In this paper, we explore the generality of this phenomenon and make the following contributions. First, we prove a super-linear lower bound on the dimension (in terms of sample size) required for support vector proliferation in independent feature models, matching the upper bounds from previous works. We further identify a sharp phase transition in Gaussian feature models, bound the width of this transition, and give experimental support for its universality. Finally, we hypothesize that this phase transition occurs only in much higher-dimensional settings in the $\ell_1$ variant of the SVM, and we present a new geometric characterization of the problem that may elucidate this phenomenon for the general $\ell_p$ case.

We propose a new technique for the automatic generation of optimal ad-hoc ans\"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

As the marketing of almost every advanced cybersecurity product will tell you, artificial intelligence is already being used in many products and services that secure computing infrastructure. But you probably haven't heard much about the need to secure the machine learning applications that are becoming increasingly widespread in the services you use day-to-day. Whether we recognize it or not, AI applications are already shaping our consciousness. Machine learning-based recommendation mechanisms on platforms like YouTube, Facebook, TikTok, Netflix, Twitter, and Spotify are designed to keep users hooked to their platforms and engaged with content and ads. These systems are also vulnerable to abuse via attacks known as data poisoning.

Handwritten digit classification is one of the multiclass classification problem statements. In this article, we'll introduce the multiclass classification using Support Vector Machines (SVM). We'll first see what exactly is meant by multiclass classification, and we'll discuss how SVM is applied for the multiclass classification problem. "How can you extend a binary classifier to a multi-class classifier in case of SVM algorithm?" Multiclass Classification: In this type of classification, the machine learning model should classify an instance as only one of three classes or more.

Abstract-- Applications that need to sense, measure, and gather real-time information from the environment frequently face three main restrictions: power consumption, cost, and lack of infrastructure. Most of the challenges imposed by these limitations can be better addressed by embedding Machine Learning (ML) classifiers in the hardware that senses the environment, creating smart sensors able to interpret the low-level data stream. However, for this approach to be cost-effective, we need highly efficient classifiers suitable to execute in unresourceful hardware, such as low-power microcontrollers. In this paper, we present an open-source tool named EmbML - Embedded Machine Learning that implements a pipeline to develop classifiers for resource-constrained hardware. We describe its implementation details and provide a comprehensive analysis of its classifiers considering accuracy, classification time, and memory usage. Moreover, we compare the performance of its classifiers with classifiers produced by related tools to demonstrate that our tool provides a diverse set of classification algorithms that are both compact and accurate. Therefore, these smart sensors are more powerefficient since they eliminate the need for communicating all the raw data. PPLICATIONS that need to sense, measure, and gather real-time information from the environment frequently of interest - e.g., a dry soil crop area that needs watering or face three main restrictions [1]: power consumption, cost, the capture of a disease-vector mosquito.

Consider a learning algorithm, which involves an internal call to an optimization routine such as a generalized eigenvalue problem, a cone programming problem or even sorting. Integrating such a method as a layer(s) within a trainable deep neural network (DNN) in an efficient and numerically stable way is not straightforward -- for instance, only recently, strategies have emerged for eigendecomposition and differentiable sorting. We propose an efficient and differentiable solver for general linear programming problems which can be used in a plug and play manner within DNNs as a layer. Our development is inspired by a fascinating but not widely used link between dynamics of slime mold (physarum) and optimization schemes such as steepest descent. We describe our development and show the use of our solver in a video segmentation task and meta-learning for few-shot learning. We review the existing results and provide a technical analysis describing its applicability for our use cases. Our solver performs comparably with a customized projected gradient descent method on the first task and outperforms the differentiable CVXPY-SCS solver on the second task. Experiments show that our solver converges quickly without the need for a feasible initial point. Our proposal is easy to implement and can easily serve as layers whenever a learning procedure needs a fast approximate solution to a LP, within a larger network.

In this tutorial will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money! Actually this program is really simple and I doubt any major profit will be made from this program, but it's slightly better than guessing! In this video will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money!

Tune a machine learning model's hyperparameters and evaluate its performance.