Finding the relationship between two coordinate systems using pairs of measurements of the coordinates of a number of points in both systems is a classic photogrammetric task. It finds applications in stereophotogrammetry and in robotics. I present here a closed-form solution to the least

We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling

perspective. These practical issues are then examined in the context of a case study in which TD(lambda) is applied to learning the game of backgammon from the outcome of self-play. This is apparently the first application of this algorithm to a complex nontrivial task. It is found that, with zero knowledge

of the toolkit. We outline the conventional language modeling technology, as implemented in the toolkit, and describe the extra efficiency and functionality that the new toolkit provides as compared to previous software for this task. Finally,we give an example of the use of the toolkit in constructing

at demonstrating the usefulness of this transformation for image ltering and segmentation tasks. Lastly, the paper focuses on implementation issues: the standard parallel and sequential approaches to reconstruction are brie y recalled; their common drawback is their ine ciency on conventional computers. To improve

of media and attributes to achieve similar learning results for any given student and learning task. This replaceability test is the key to my argument since if a treatment can be replaced by another treatment with similar results, the cause of the results is in some shared (and uncontrolled) properties

Gaussian-mixturedistributionmodeling.Bytrainingthenetworktogeneratethesubwordprobabilityposteriors, thenusingtransformationsoftheseestimatesasthebasefeatures foraconventionally-trainedGaussian-mixturebasedsystem,we achieverelativeerrorratereductions of 35% or mor eonthemulticondition Aurora noisy continuous digits task.

Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two

We envision a future where people’s living spaces are interactive and programmable. Users interact with their offices, homes, cars, malls and airports to request information, benefit from the resources available, and configure the habitat’s behavior. Data and tasks are always accessible

Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All