Use neighborhood component analysis (NCA) to choose features for machine learning models. Manipulate and analyze data that is too big to fit in memory. Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data. Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters. Manipulate, compare, and store text data efficiently .

This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easily applied to standard single task learning, as well. As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past. We present a Rademacher complexity bound based on which a new Multi-Task Multiple Kernel Learning (MT-MKL) model is derived. In particular, we propose a Support Vector Machine-regularized model in which, for each task, an optimal kernel is learned based on a neighborhood-defining kernel that is not restricted to be positive semi-definite. Comparative experimental results are showcased that underline the merits of our neighborhood-defining framework in both classification and regression problems.

Within kernel methods, kernel adaptive filters (KAFs) [1] are state-of-the-art nonlinear models for time series that build on the properties of reproducing kernel Hilbert spaces (RKHS) [2], in order to provide accurate predictions at a low computational cost. In the same way that support vectors play a fundamental role in support vector machines [3], KAFs rely on a subset of observed input samples referred to as centres, where new inputs are compared to these centres through a kernel function to compute the prediction. This procedure involves a number of parameters: those of the kernel, those related to the selection of the set of centres (dictionary), and those controlling the tradeoff between historical data and new observations. By adapting these model parameters, algorithms, such as kernel least mean square (KLMS) [4], [5] provide an efficient way to improve signal estimation over time as more data become available. Specifically, KLMS applies the least-mean-square rationale to the "kernelised" input (i.e., transformed by the kernel function), thus allowing for an efficient online implementation based on gradient steepest descent for updating the model parameters (i.e., the filter weights only). The main drawback of KAFs is the lack of a principled approach to tune filter weights, kernel parameters and the dictionary.

In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem. In Python, scikit-learn is a widely used library for implementing machine learning algorithms, SVM is also available in scikit-learn library and follow the usual structure (Import library, object creation, fitting model and prediction). As pointed out by Admiral deblue in the comments below, all practical implementations of SVMs have strict requirements for training and testing (prediction).

Driverless AI is the latest product from H2O.ai aimed at lowering the barrier to making data science work in a corporate context. The tool assists non-technical employees with preparing data, calibrating parameters and determining the optimal algorithms for tackling specific business problems with machine learning. At the research level, machine learning problems are complex and unpredictable -- combining GANs and reinforcement learning in a never before seen use case takes finesse. But the reality is that a lot of corporates today use machine learning for relatively predictable problems -- evaluating default rates with a support vector machine, for example. But even these relatively straightforward problems are tough for non-technical employees to wrap their heads around.

Driverless AI is the latest product from H2O.ai aimed at lowering the barrier to making data science work in a corporate context. The tool assists non-technical employees with preparing data, calibrating parameters and determining the optimal algorithms for tackling specific business problems with machine learning. At the research level, machine learning problems are complex and unpredictable -- combining GANs and reinforcement learning in a never before seen use case takes finesse. But the reality is that a lot of corporates today use machine learning for relatively predictable problems -- evaluating default rates with a support vector machine, for example. But even these relatively straightforward problems are tough for non-technical employees to wrap their heads around.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed techniques are based on Random Fourier Features and the Nystr\"om methods. The resulting formulations can be solved with efficient stochastic approximation techniques such as stochastic (sub)-gradient, stochastic proximal gradient techniques or their variants.

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for classification as well as regression challenges. It is said to be one of the most popular high-performance algorithms and is implemented in practice using a kernel. In this algorithm, the dataset explains SVM about classes so that it can classify new data. It works by classifying data through finding the line which separates data into classes. It tries to maximise the distance between the various classes and referred as margin maximisation. SVM can be classified into two categories one is Linear SVM in which classifiers are separated by hyperplane and other i.e. non-linear SVM which is applicable for a more complex task which cannot separate training data using hyperplane.

Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the \emph{first} \emph{static} feature and sample reduction method for sparse SVM. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.