by an intimidating, competitive confederate). This result suggests that agents can represent self-generated and other-generated actions separately, but tend to relate or integrate these representations if the personal relationship between self and other has a positive valence. Tasks play a crucial role in people’s

We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and non-rigid objects such as hands.

Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text

Abstract not found

allow us to model large data sets, consisting of millions of surface points, by a single RBF---previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] algorithm as being the most resistant to common image

proposed in recent years. This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional datu, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts).

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many

Several parametric representations of the acoustic signal were compared as to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary in-cluded many phonetically similar monosyllabic words, therefore the emphasis was on ability to retain

’ ’ or ‘‘executive’ ’ tasks. One hun-dred thirty-seven college students performed a set of relatively simple experimental tasks that are considered to predominantly tap each target executive function as well as a set of frequently used executive tasks: the Wisconsin Card Sorting Test (WCST), Tower of Hanoi (TOH