The mathematical relationship between the expectedconfusion metric and the area under a receiver operating characteristic (ROC) curve is derived. Given a limited database of subjects and an identification technique that generates a feature vector per subject, expected confusion is used to predict how well the feature vector will filter identity in a larger population. Related is the area under ...

We consider the problem of detecting whether a high dimensional vector ∈ R lies in a r-dimensional subspace S, where r n, given few compressive measurements of the vector. This problem arises in several applications such as detecting anomalies, targets, interference and brain activations. In these applications, the object of interest is described by a large number of features and the ability to...

Several approaches have been proposed to circumvent the impossibility to solve consensus in asynchronous distributed systems prone to process crash failures. Among them, randomization, unreliable failure detectors, and leader oracles have been particularly investigated. Recently a new approach (called “condition-based”) has been proposed. Let an input vector be a vector whose i-th entry contain...

We introduce a class of exible conditional probability models and techniques for classi cation regression problems Many existing methods such as generalized linear models and support vector machines are subsumed under this class The exibility of this class of techniques comes from the use of kernel functions as in support vector machines and the generality from dual formulations of stan dard re...

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent t...

This procedure may be actually called minimum distance decoding, the name “maximum likelihood” is justified in a short while. Intuitively is seems natural to assume that this decoding is the best we can hope for in terms of minimizing the error rate. Note if a vector x ∈ C (say a binary code) is sent over a binary symmetric channel W , the probability that a vector y is received on its output P...

The problem of binary classification can be stated as follows. We have a random couple Z = (X ,Y ), where X ∈ Rd is called the feature vector and Y ∈ {−1,1} is called the label1. In the spirit of the modelfree framework, we assume that the relationship between the features and the labels is stochastic and described by an unknown probability distribution P ∈P (Z), where Z=Rd × {−1,1}. As usual, ...

Support Vector Machine based on Time Series (SVM-TS) was applied to predict soil moisture and nitrate nitrogen (NO3--N) content. For the prediction of soil moisture, the statistical result (t-test) indicate that there is no obvious difference between predicted and observed values in 0-20cm and 20-60cm soil layers, and that SVM-TS is capable for soil moisture prediction. For the prediction of NO...

Based on concentration probability of estimators about a true parameter, third-order asymptotic e ciency of the rst-order bias-adjusted MLE within the class of rst-order bias-adjusted estimators has been well established in a variety of probability models. In this paper we consider the class of second-order bias-adjusted Fisher consistent estimators of a structural parameter vector on the basis...