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Large Margin Methods for Structured and Interdependent Output Variables
TLDR
This paper proposes to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation and presents a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. Expand
Support Vector Machines for Multiple-Instance Learning
TLDR
The proposed extensions of the Support Vector Machine learning approach lead to mixed integer quadratic programs that can be solved heuristic ally and a generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. Expand
Support vector machine learning for interdependent and structured output spaces
TLDR
This paper proposes to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs, and demonstrates the versatility and effectiveness of the method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment. Expand
Probabilistic latent semantic indexing
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a trainingExpand
Probabilistic Latent Semantic Analysis
TLDR
This work proposes a widely applicable generalization of maximum likelihood model fitting by tempered EM, based on a mixture decomposition derived from a latent class model which results in a more principled approach which has a solid foundation in statistics. Expand
Unsupervised Learning by Probabilistic Latent Semantic Analysis
TLDR
This paper proposes to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice, and results in a more principled approach with a solid foundation in statistical inference. Expand
Hidden Markov Support Vector Machines
TLDR
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which it is called HM-SVMs and handles dependencies between neighboring labels using Viterbi decoding. Expand
Probabilistic Latent Semantic Indexing
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a trainingExpand
Latent semantic models for collaborative filtering
TLDR
A new family of model-based algorithms designed for collaborative filtering rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. Expand
Beyond sliding windows: Object localization by efficient subwindow search
TLDR
A simple yet powerful branch-and-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages and converges to a globally optimal solution typically in sublinear time is proposed. Expand
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