In a typical adaptive update law, the rate of adaptation is generally a function of the state feedback error. Ideally, the adaptive update law would also include some feedback of the parameter estimation error. The desire to include some measurable form of the parameter estimation error in the adaptation law resulted in the development of composite adaptive update laws that are functions of a p...

this study was performed with objective land cover change prediction (forest, agriculture, residential and orchard) using logistic regression and markov chain in the gis environment in east of mazandaran province. land cover change detected using satellite imageries belonging to the years 1987 and 2001. transition potential modeling was conducted using a logistic regression. seven variables (de...

Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of prediction errors (mislabeling, representation, learner and boundary errors) and demonstrate that these four types characterize all prediction errors. In addition, we describe potential remedies and tools that can be used to reduce the u...

Making predictions about future rewards is an important ability for primates, and its neurophysiological mechanisms have been studied extensively. One important approach is to identify neural systems that process errors related to reward prediction (i.e., areas that register the occurrence of unpredicted rewards and the failure of expected rewards). In monkeys that have learned to predict appet...

Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluate its prediction accuracy. In this paper, we develop procedures for obtaining both point and interval estimators for the prediction error. Under mild regularity conditions, we derive the consistency and asymptotic norm...

Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, throug...

This paper tries to provide a new perspective for the research of reversible watermarking based on histogram shifting of prediction errors. Instead of obtaining one prediction error for the current pixel, we calculate multiple prediction errors by designing a multi-prediction scheme. An asymmetric error histogram is then constructed by selecting the suitable one from these errors. Compared with...

A new method for texture coding which combines 2-D linear prediction and stochastic vector quantization is presented in this paper. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ, using an algorithm which takes into account the pixels surrounding the block being encoded. In...

The mammalian medial frontal cortex (MFC) is involved in reward-based decision making. In particular, in nonhuman primates this area constructs expectations about upcoming rewards, given an environmental state or a choice planned by the animal. At the same time, in both humans and nonhuman primates, the MFC computes the difference between such predictions and actual environmental outcomes (rewa...

Animal research and human brain imaging findings suggest that reward processing involves distinct anticipation and outcome phases. Error terms in popular models of reward learning (such as the temporal difference [TD] model) do not distinguish between the updating of expectations in response to reward cues and outcomes. Thus, correlating a single error term with neural activation assumes recrui...