We present a novel nearest neighbor rule-based implementation of the structural risk minimization principle to address a generic classification problem. We propose a fast reference set thinning algorithm on the training data set similar to a support vector machine approach. We then show that the nearest neighbor rule based on the reduced set implements the structural risk minimization principle...

An alternate approach to emergency risk minimization, based on lazy calculations and high-level functional interpretation is presented in this article. This approach is compared with classical imperative approach, based on statistical analysis on real data in order to prove its effectiveness.

This paper studies a new framework for learning a predictor in the presence of multiple kernel functions where the learner selects or extracts several kernel functions from potentially complex families and finds an accurate predictor defined in terms of these functions. We present an algorithm, Voted Kernel Regularization, that provides the flexibility of using very complex kernel functions suc...

Classification trees are one of the most popular types of classifiers, with ease of implementation and interpretation being among their attractive features. Despite the widespread use of classification trees, theoretical analysis of their performance is scarce. In this paper, we show that a new family of classification trees, called dyadic classification trees (DCTs), are near optimal (in a min...

This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise and the estimates is unbounded. The bound applies to many loss functions besides the squared loss, and scales only with the sub-Gaussian or subexponential parameters without further statistical assumptions such as the...

This paper presents a novel probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We di...

We propose what appears to be the first anomaly detection framework that learns from positive examples only and is sensitive to substantial differences in the presentation and penalization of normal vs. anomalous points. Our framework introduces a novel type of asymmetry between how false alarms (misclassifications of a normal instance as an anomaly) and missed anomalies (misclassifications of ...

We apply the quadratic hedging scheme developed by Föllmer, Schweizer, and Sondermann to European contingent products whose underlying asset is modeled using a GARCH process. The main contributions of this work consist of showing that local risk-minimizing strategies with respect to the physical measure do exist, even though an associated minimal martingale measure is only available in the pres...

Candidate selection from n-best lists is a widely used approach in natural language parsing. Instead of attempting to select the most probable candidate, we focus on prediction of a new structure which minimizes an approximation to Bayes risk. Our approach does not place any restrictions on the probabilistic model used. We show how this approach can be applied in both dependency and constituent...

We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction...