Support Vector Machines

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.

Logistic regression was once the most popular machine learning algorithm, but the advent of more accurate algorithms for classification such as support vector machines, random forest, and neural networks has induced some machine learning engineers to view logistic regression as obsolete. Though it may have been overshadowed by more advanced methods, its simplicity makes it the ideal algorithm to use as an introduction to the study of machine learning. Like most machine learning algorithms, logistic regression creates a boundary edge between binary labels. The purpose of a training process is to place this edge in such a way that most of the labels are divided so as to maximize the accuracy of predictions. The training process requires correct model architecture and fine-tuned hyperparameters, whereas data play the most significant role in determining the prediction accuracy.

In Machine learning, classification problems with high-dimensional data are really challenging. Sometimes, very simple problems become extremely complex due this'curse of dimensionality' problem. In this article, we will see how accuracy and performance vary across different classifiers. We will also see how, when we don't have the freedom to choose a classifier independently, we can do feature engineering to make a poor classifier perform well. For this article, we will use the "EEG Brainwave Dataset" from Kaggle.

One of the problems with these algorithms and the features they leverage is that they are based on correlational relationships that may not be causal. As Russ states: "Because there could be a correlation that's not causal. And I think that's the distinction that machine learning is unable to make--even though "it fit the data really well," it's really good for predicting what happened in the past, it may not be good for predicting what happens in the future because those correlations may not be sustained." This echoes a theme in a recent blog post by Paul Hunermund: "All of the cutting-edge machine learning tools--you know, the ones you've heard about, like neural nets, random forests, support vector machines, and so on--remain purely correlational, and can therefore not discern whether the rooster's crow causes the sunrise, or the other way round" I've made similar analogies before myself and still think this makes a lot of sense. However, a talk at the International Conference on Learning Representations definitely made me stop and think about the kind of progress that has been made in the last decade and the direction research is headed.

Steinwart was the first to prove universal consistency of support vector machine classification. His proof analyzed the'standard' support vector machine classifier, which is restricted to binary classification problems. In contrast, recent analysis has resulted in the common belief that several extensions of SVM classification to more than two classes are inconsistent. Our proof extends Steinwart's techniques to the multi-class case. Papers published at the Neural Information Processing Systems Conference.

Would people who are strong in math be good in machine learning? Certainly having a strong background in mathematics will make it easier to understand machine learning at a conceptual level. When someone introduces you to the inference function in logistic regression, you'll say, "Hey, that's just linear algebra!" But surely deep learning must be something new? Not harder, just more (thank God for automatic differentiation).

Statistical learning theory has been studied for general function estimation from data since the late 1960's [22]. However, it was only widely adopted in practice after the introduction of the learning algorithms known as Support Vector Machines (SVMs) [23]. Using the so-called kernel trick, which replaces dot products between features and model parameters by evaluations of a kernel function, SVMs can learn nonlinear relations from training patterns by solving a convex optimization problem [16]. An important variant of the SVM is the Least Squares Support Vector Machine (LS-SVM) [20], which is obtained by making all data points supportvectors. LS-SVM avoids the constrained quadratic optimization step of standard SVMs by replacing the training procedure with one that reduces to solving a system of linear equations, which can be performed via ordinary least squares. The first SVM formulation was derived for classification tasks, but it has been readily adapted to tackle regression problems, being usually named Support Vector Regression (SVR) [6]. Similarly, the regression counterpart of LS-SVM is the LS-SVR [20]. 1

The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning. In this paper, we propose a distributed SVM algorithm and demonstrate its use in a number of applications. The algorithm is named high-performance support vector machines (HPSVM). The major contribution of HPSVM is two-fold. First, HPSVM provides a new way to distribute computations to the machines in the cloud without shuffling the data. Second, HPSVM minimizes the inter-machine communications in order to maximize the performance. We apply HPSVM to some real-world classification problems and compare it with the state-of-the-art SVM technique implemented in R on several public data sets. HPSVM achieves similar or better results.

In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect answers for both 2D and 3D questions. Using the classification technique, the impacts of 2D and 3D multimedia educational contents on learning, memory retention and recall will be compared. The subjects learn similar 2D and 3D educational contents. Afterwards, subjects are asked 20 multiple-choice questions (MCQs) associated with the contents after thirty minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers. Excellent accuracies obtained by both classifiers and by applying statistical analysis on the results, no significant difference is indicated between 2D and 3D multimedia educational contents on learning, memory retention and recall in both STM and LTM.