Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies. In this paper, an attempt has been made to classify lithology using a multiclass SVM based framework using well logs as predictor variables. Here, the lithology is classified into four classes such as sand, shaly sand, sandy shale and shale based on the relative values of sand and shale fractions as suggested by an expert geologist. The available dataset consisting well logs (gamma ray, neutron porosity, density, and P-sonic) and class information from four closely spaced wells from an onshore hydrocarbon field is divided into training and testing sets. We have used one-against-all strategy to combine the results of multiple binary classifiers. The reported results established the superiority of multiclass SVM compared to other classifiers in terms of classification accuracy. The selection of kernel function and associated parameters has also been investigated here. It can be envisaged from the results achieved in this study that the proposed framework based on multiclass SVM can further be used to solve classification problems. In future research endeavor, seismic attributes can be introduced in the framework to classify the lithology throughout a study area from seismic inputs.

Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context, this paper proposes a generalized Support Vector Data Description (SVDD) based novel classification framework to classify water saturation into two classes (Class high and Class low) from three seismic attributes seismic impedance, amplitude envelop, and seismic sweetness. G-metric means and program execution time are used to quantify the performance of the proposed framework along with established supervised classifiers. The documented results imply that the proposed framework is superior to existing classifiers. The present study is envisioned to contribute in further reservoir modeling.

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) to classify a reservoir characteristic water saturation into two classes (Class high and Class low) from four logs namely gamma ray, neutron porosity, bulk density, and P sonic using an imbalanced dataset. A comparison is carried out among proposed framework and different supervised classification algorithms in terms of g metric means and execution time. Experimental results show that proposed framework has outperformed other classifiers in terms of these performance evaluators. It is envisaged that the classification analysis performed in this study will be useful in further reservoir modeling.

Although kernel methods such as support vector machines are one of the state-of-the-art methods when it comes to fully automated learning, see e.g. the recent independent comparison [7], the recent years have shown that on complex datasets such as image, speech and video data, they clearly fall short compared to deep neural networks. One possible explanation for this superior behavior is certainly their deep architecture that makes it possible to represent highly complex functions with relatively few parameters. In particular, it is possible to amplify or suppress certain dimensions or features of the input data, or to combine features to new, more abstract features. Compared to this, standard kernels such as the popular Gaussian kernels simply treat every feature equally. In addition, most users of kernel machines probably stick to the very few standard kernels, often simply because there is in most cases no principled way for finding problem specific kernels.

The field of pathology diagnosis has steadily advanced with the development of microscopy, accompanied by the automation of the reduction of inter-observer reliability and intra-observer reproducibility. Within the field of mammography, computer vision, and artificial intelligence (AI) techniques have been successfully applied to detect and characterize abnormalities of medical images [Winsberg et al., 1967; Ravdin et al., 2001]. This has resulted in a situation such that automated detection techniques can now implement an entire medical procedure with a high degree of accuracy. In addition, advances in computer hardware and software have increased the performance and reliability of parallel computing. The advances in this technology have, in turn, provided hardware and software advancements that are sufficiently robust to support the large computational requirements of complex Artificial Intelligence (AI) algorithms and their application to machine learning.

ABSTRACT Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit nonlinear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard K-means on the transformed data. The analysis and experiments suggest the method is accurate, while requiring drastically less memory than standard kernel K-means and significantly less memory than Nyström based approximations. Index Terms-- Kernel methods, Unsupervised learning, Lowrank approximation, Randomized algorithm 1. INTRODUCTION Kernel-based approaches are popular methods for supervised and unsupervised learning [1].

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. As such, we've adopted a pragmatic approach to the machine learning process, based on a series of iterative experiments and relying on evidence-based decision making to answer the most important business questions. In this series of posts, we're going to look at what machine learning really is (and isn't), the experimentation process and some real examples of how and where we've put it to use. It's no exaggeration to say that the hype around machine learning has gone a bit crazy recently. Since the explosion of Big Data, the need to make sense of masses of digital information has not surprisingly increased, causing a wave of excitement around a new era of data science.

A lot of Machine Learning (ML) projects consist of fitting a (normally very complicated) function to a dataset with the objective of calculating a number like 1 or 0 (is it spam or not?) for classification problems or a set of numbers (e.g., weekly sales of a product) for regression ones. Yes, it's all about numbers and loads of operations which a computer is very good at. Consider the gender recognition by voice dataset which can be found in this Kaggle page. The objective with this dataset is, when given a speech signal, to identify whether it is from a male or female. This challenge falls under the category of a classification problem.

We've already published the top big data presentations on slideshare, as well as great Github list of public data sets, or top machine learning projects, or top R packages. We've asked our readers to share a list of top Data Science videos on YouTube. Here, we share a list of top data science presentations from VideoLectures.net. These presentations received 5 to 20 times fewer page views than those on Slideshare, because they are far more technical, and attract a different, truly technical audience. You can check the entire list here.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. For the last one I'd let you pick one of the following: For the last one I'd let you pick one of the following: My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.