Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

We present an approach to teach incrementally human gestures to a humanoid robot. By
using active teaching methods that puts the human teacher" in the loop" of the robot's …

We propose a novel approach to online estimation of probability density functions, which is
based on kernel density estimation (KDE). The method maintains and updates a non …

In open-world learning, an agent starts with a set of known classes, detects, and manages
things that it does not know, and learns them over time from a non-stationary stream of data …

Gait analysis provides a feasible approach for identification in intelligent video surveillance.
However, the effectiveness of the dominant silhouette-based approaches is overly …

In this paper, we extend the conventional vector quantization by incorporating a vigilance
parameter, which steers the tradeoff between plasticity and stability during incremental …

How to construct models for speech/nonspeech discrimination is a crucial point for voice
activity detectors (VADs). Semi-supervised learning is the most popular way for model …

As the capacity for machines to extend human capabilities continues to grow, the
communication channels used must also expand. Allowing machines to interpret nonverbal …

This paper presents a new algorithm for unsupervised incremental learning based on a
Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model) …

We introduce an online outlier detection algorithm to detect outliers in a sequentially
observed data stream. For this purpose, we use a two-stage filtering and hedging approach …