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Locally Weighted Learning
TLDR
The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, and applications of locally weighted learning.
Cyberguide: A mobile context‐aware tour guide
TLDR
The Cyberguide project is presented, in which the authors are building prototypes of a mobile context‐aware tour guide that is used to provide more of the kind of services that they come to expect from a real tour guide.
Constructive Incremental Learning from Only Local Information
TLDR
A constructive, incremental learning system for regression problems that models data by means of spatially localized linear models that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way is introduced.
The Aware Home: A Living Laboratory for Ubiquitous Computing Research
TLDR
The Aware Home project is introduced and some of the technology-and human-centered research objectives in creating the Aware Home are outlined, to create a living laboratory for research in ubiquitous computing for everyday activities.
Kinematic features of unrestrained vertical arm movements
TLDR
Unrestrained human arm trajectories between point targets have been investigated using a three-dimensional tracking apparatus, the Selspot system, and movement regions were discovered in which the hand paths were curved.
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time
TLDR
This work presents a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems, which successfully solves large state-space real-time problems with which other methods have difficulty.
Locally Weighted Learning for Control
TLDR
There are ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks, and various forms that control tasks can take, are explained.
Predicting human interruptibility with sensors
TLDR
This article presents a series of studies that quantitatively demonstrate that simple sensors can support the construction of models that estimate human interruptibility as well as people do, and therefore their use in everyday office environments is both practical and affordable.
Robot Learning From Demonstration
TLDR
This work has shown that incorporating a task level direct learning component, which is non-model-based, in addition to the model-based planner, is useful in compensating for structural modeling errors and slow model learning.
Human-in-the-loop optimization of exoskeleton assistance during walking
TLDR
A method for identifying the exoskeleton assistance that minimizes human energy cost during walking is developed, which was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity.
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