AIMC Topic: Artificial Intelligence

Clear Filters Showing 19561 to 19570 of 26332 articles

Sample Selection for Training Cascade Detectors.

PloS one
Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represe...

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

Animal : an international journal of animal bioscience
In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet...

Computational rationality: A converging paradigm for intelligence in brains, minds, and machines.

Science (New York, N.Y.)
After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared view of the computational foundations of intellig...

Economic reasoning and artificial intelligence.

Science (New York, N.Y.)
The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the my...

Policy forum. Data, privacy, and the greater good.

Science (New York, N.Y.)
Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhanci...

Decentralized Sliding Mode Observer Based Dual Closed-Loop Fault Tolerant Control for Reconfigurable Manipulator against Actuator Failure.

PloS one
This paper considers a decentralized fault tolerant control (DFTC) scheme for reconfigurable manipulators. With the appearance of norm-bounded failure, a dual closed-loop trajectory tracking control algorithm is proposed on the basis of the Lyapunov ...

Maximum margin semi-supervised learning with irrelevant data.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of...

Multiple Sparse Representations Classification.

PloS one
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dicti...