where 〈w,x〉 is the inner product between the vectors x and w. For the 0-1 transfer function, φ0−1(a) = sgn(a)+1 2 , H becomes the class of halfspaces. We allow any transfer functions that satisfy the following (μ, ) margin condition: max{|φ(a)− φ0−1(a)| : |a| > μ} ≤ . For example, the sigmoid function φsig(a) = 1 1+e−a/σ satisfies the (μ, ) condition if σ ≤ μ/(log(1/ − 1). For an illustration s...

In this paper, the structural risk minimization (SRM) criterion is employed to train a large margin classifier, the support vector machine (SVM). Its relative performance is compared with traditional classifiers employing hyperplanes against a realistic difficult problem, the synthetic aperture radar (SAR) automatic target recognition (ATR). In most pattern recognition applications, the task is...

The introduction of large-margin based discriminative methods for optimizing statistical machine translation systems in recent years has allowed exploration into many new types of features for the translation process. By removing the limitation on the number of parameters which can be optimized, these methods have allowed integrating millions of sparse features. However, these methods have not ...

1Faculty of Chemical Engineering, Babol University of Technology, PO Box, 484, Babol, Iran 2Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection,“Gheorghe Asachi” Technical University of Iaşi, Str. Prof. Dr. Doc. DimitrieMangeron, nr. 73, 700050, Iaşi, Romania 3Department of Computer Science and Engineering, Faculty of Automatic Control and Computer E...

In this paper, we present a novel discriminative training method for multinomial mixture models (MMM) in text categorization based on the principle of large margin. Under some approximation and relaxation conditions, large margin estimation (LME) of MMMs can be formulated as linear programming (LP) problems, which can be efficiently and reliably solved by many general optimization tools even fo...

The proposed method uses homonymous and heteronymous examplepairs to train a linear preprocessor on a kernel-induced Hilbert space. The algorithm seeks to optimize the expected performance of elementary classi ers to be generated from single future training examples. The method is justi ed by PAC-style generalization guarantees and the resulting algorithm has been tested on problems of geometri...

We propose a max-margin formulation for the multi-label classification problem where the goal is to tag a data point with a set of pre-specified labels. Given a set of L labels, a data point can be tagged with any of the 2 possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of t...

KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to l...

Several learning algorithms in classification and structured prediction are formulated as large scale optimization problems. We show that a generic iterative reformulation and resolving strategy based on the progressive hedging algorithm from stochastic programming results in a highly parallel algorithm when applied to the large margin classification problem with nonlinear kernels. We also unde...

We present an open-source framework for large-scale online structured learning. Developed with the flexibility to handle cost-augmented inference problems such as statistical machine translation (SMT), our large-margin learner can be used with any decoder. Integration with MapReduce using Hadoop streaming allows efficient scaling with increasing size of training data. Although designed with a f...