Various problems in nonnegative quadratic programming arise in the training of large margin classifiers. We derive multiplicative updates for these problems that converge monotonically to the desired solutions for hard and soft margin classifiers. The updates differ strikingly in form from other multiplicative updates used in machine learning. In this paper, we provide complete proofs of conver...

Simultaneous risk-limiting audits of a collection of contests have a known minimum chance of leading to a full hand count if any outcomes are wrong. They are generally performed in stages. Each stage involves drawing a sample of ballots, comparing a hand count of the votes on those ballots with the original count, and assessing the evidence that the original outcomes agree with the outcomes tha...

In this paper, we evaluate the stability margin of a Fiat Dedra engine model to explore possible applications of theoretical achievements in the parametric approach to robust control problems. The study is based on two versions of the model with uncertainties. One is the original model, which has a multilinear characteristic polynomial, while the other is an affine-linearized model which is obt...

In this paper, we investigate the read operation of memristor-based memories. We analyze the sneak paths problem and provide a noise margin metric to compare the various solutions proposed in the literature. We also analyze the power consumption associated with these solutions. Moreover, we study the effect of the aspect ratio of the memory array on the sneak paths. Finally, we introduce a new ...

Sauer’s Lemma is extended to classes H of binary-valued functions on [n] = {1, . . . , n} which have a margin less than or equal to N on x ∈ [n], where the margin μh(x) of a binary valued function h at a point x ∈ [n] is defined as the largest nonnegative integer a such that h is constant on the interval Ia(x) = [x−a, x+a] ⊆ [n]. Estimates are obtained for the cardinality of classes of binary v...

Previous results have shown disappointing performance when porting a parser trained on one domain to another domain where only a small amount of data is available. We propose the use of data-defined kernels as a way to exploit statistics from a source domain while still specializing a parser to a target domain. A probabilistic model trained on the source domain (and possibly also the target dom...

This paper details a systematic method for significantly improving the noise margins of very fast threshold gates. The method is based on adding nonlinear terms determined from the Boolean form of the threshold function to be implemented. Simulation results support our theoretical claims. Finally, two methods for drastically reducing the dissipated power of such threshold gates down to <50%, an...

Mobile manipulators operating in field environments will be required to apply large forces, or manipulate large loads, and to perform such tasks on uneven terrain which may cause the system to approach, or reach, a dangerous tipover instability. To avoid tipover in an automatic system, or to provide a human operator with an indication of proximity to tipover, it is necessary to define a measure...

Margin theory provides one of the most popular explanations to the success of AdaBoost, where the central point lies in the recognition that margin is the key for characterizing the performance of AdaBoost. This theory has been very influential, e.g., it has been used to argue that AdaBoost usually does not overfit since it tends to enlarge the margin even after the training error reaches zero....

Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution r...