We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization problem involving a low-rank Hankel matrix that represents the inputoutput operator model. The direct outcome of our work is a new spectral reg...

Optimal tracking is considered for a linear time-invariant plant in data space. The proposed control strategy does not employ any traditional mathematical model such as a transfer function or a state-space equation. Instead, the plant dynamics is represented as a set of basis vectors whose elements are inputoutput data of the plant. Using this system representation, an optimal tracking problem ...

Estimating the conditional mean of an inputoutput relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of condition...

We study the probability distributions of difference propagation probabilities and inputoutput correlations for functions and block ciphers of given dimensions, for several of them for the first time. We show that these parameters have distributions that are well-studied in the field of probability such as the normal, Poisson and extreme value distributions. The results of this paper can be use...

Besides their typical classification task, Self-Organizing Maps (SOM) can be used to approximate inputoutput relations. They provide an economic way of storing the essence of past data into input/output support vector pairs. In this paper the SOLIM algorithm (Self-Organising Locally Interpolating Map) is reviewed and an extrapolation method is introduced. This framework allows finding one inver...

System modeling concerns modeling the operation of an unknown system from a set of measured input-output data and/or some prior knowledge (e.g., experience, expertise, or heuristics) about the system. It plays a very important role and has a wide range of applications in various areas such as control, power systems, communications, networks, machine intelligence, etc. To understand the underlyi...

We consider a frequency-domain solution to the leastsquares equation error identi cation problem using the power spectrum and the cross-spectrum of the IO (inputoutput) data to estimate the IO parametric transfer function. The proposed approach is shown to yield a unimodal performance surface, consistent identi cation in colored noise and su cient-order case, and stable tted models under underm...

In most control and engineering applications, the use of fuzzy system as a way to improve the humtzn-computer interaction has becoming popular. This paper reports on the use of fuzzy system in mineral processing specifically in determining the parameter d50c of hydrocyclone. However, wit,h the inputoutput data provided to build the fuzzy rule base, it normally results in a sparse fuzzy rule bas...

In this paper, we suggested an utility learning algorithm for tuning fuzzy rules by using training inputoutput data, based on the gradient descent method. The major advantage of this method is that fuzzy rules or membership functions can be learned without changing the form of the fuzzy rule table used in usual fuzzy controls, so that the case of weak-firing can be avoided well, which is differ...

Unstable dynamical systems can be viewed from a variety of perspectives. We discuss the potential of an inputoutput map associated with an unstable system to represent a bounded map from L2(R) to itself and then develop criteria for optimal reduced order approximations to the original (unstable) system with respect to an L2-induced Hilbert-Schmidt norm. Our optimality criteria extend the Meier-...