A pragmatic approach to multi-class classification

We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015, Bruges, Belgium. 201

Estimating the market share attraction model using support vector regressions.

We propose to estimate the parameters of the Market Share Attraction Model (Cooper & Nakanishi, 1988; Fok & Franses, 2004) in a novel way by using a non-parametric technique for function estimation called Support Vector Regressions (SVR)(Vapnik, 1995; Smola, 1996). Traditionally, the parameters of the Market Share Attraction Model are estimated via a Maximum Likelihood (ML) procedure, assuming that the data are drawn from a conditional Gaussian distribution. However, if the distribution is unknown, ML estimation may seriously fail (Vapnik, 1982). One way to tackle this problem is to introduce a linear loss function over the errors and a penalty on the magnitude of model coefficients. This leads to qualities such as robustness to outliers and avoidance of the problem of over¯tting. This kind of estimation forms the basis of the SVR technique, which, as we will argue, makes it a good candidate for solving the Market Share Attraction Model. We test the SVR approach to predict (the evolution of) the market shares of 36 car brands simultaneously and report stronger results than when using a ML estimation procedure.

DME Handout: Support Vector Machines School of Informatics, University of

Support Vector Machines (SVMs) are a relatively new concept in supervised learning, but since the publication of [3] in 1995 they have been applied to a wide variety of problems. In many ways the application of SVMs to almost any learning problem mirrors the enthusiasm (and fashionability) that was observed for neural networks in the second half of the 1980’s. The ingredients of the SVM had, in fact, been around for a decade or so, but they were not put together until the early 90’s. The two key ideas of support vector machines are (i) The maximum margin solution for a linear classifier. (ii) The “kernel trick”; a method of expanding up from a linear classifier to a non-linear one in an efficient manner. Below we discuss these key ideas in turn, and then go on to consider support vector regression and some example applications of SVMs. Further reading on the topic can be found in [2], [7] and [4]. For those keen to keep up with the latest results, the web sit

Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class

Conic SMO

We derive an SMO-like algorithm for the optimization problem arising from the Second Order Cone Programming formulation of support vector machines (SVMs) for classification. Due to the square root term in the objective of the form, we cannot easily compute the location of the extrema using Newton‘s formula. Instead we modify the analytic solution proposed for SMO (Platt, 1999), to account for the particular objective function and partial derivatives. We then establish some theoretical properties of the algorithm based on the results of (Bordes et al., 2005), and develop practical working set selection similar to (Fan et al., 2005). In addition, we propose an efficient implementation, and compare the convergence rate of Conic-SMO and SMO on synthetic data

Evaluation Of Machine Learning Classification Methods For Rice Detection Using Earth Observation Data: Case Of Senegal

Agriculture is considered one of the most vulnerable sectors to climate change. In addition to rainfed agriculture, irrigated crops such as rice have been developed in recent decades along the Senegal River. This new crop requires reliable information and monitoring systems. Remote sensing data have proven to be very useful for mapping and monitoring rice fields. In this study, a rice classification system based on machine learning to recognize and categorize rice is proposed. Physical interpretations of rice with other land cover classes in relation to the spectral signature should identify the optimal periods for mapping rice plots using three machine learning methods including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). The database is composed of field data collected by GPS and high spatial resolution (10 to 30 m) satellite data acquired between January and May 2018. The analysis of the spectral signature of different land cover show that the ability to differentiate rice from other classes depends on the level of rice development. The results show the efficiency of the three classification algorithms with overall accuracies and Kappa coefficients for SVM (96.2%, 94.5%), for CART (97.6%, 96.5%) and for RF (98% 97.1%) respectively. Unmixing analysis was made to verify the classification and compare the accuracy of these three algorithms according to their performance