Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognitio...

I study active learning in general pool-based active learning models as well noisy active learning algorithms and then compare them for the class of linear separators under the uniform distribution.

The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, ...

In this paper, we present an empirical study on adapting Conditional Random Fields (CRF) models to conduct semantic analysis on biomedical articles using active learning. We explore uncertaintybased active learning with the CRF model to dynamically select the most informative training examples. This abridges the power of the supervised methods and expensive human annotation cost.

This paper presents a rigorous statistical analysis characterizing regimes in which active learning significantly outperforms classical passive learning. Active learning algorithms are able to make queries or select sample locations in an online fashion, depending on the results of the previous queries. In some regimes, this extra flexibility leads to significantly faster rates of error decay t...

Modern theories of learning claim the construction of knowledge occurs as students build understanding in light of experiences occurring in the world. Experience can occur within the context of various pedagogic modes within a classroom setting; moreover, the development of deep conceptual understanding of content and the processes of science – as informed by constructivist models of learning –...

We present a hybrid machine learning approach for coreference resolution. In our method, we use CRFs as basic training model, use active learning method to generate combined features so as to make existed features used more effectively; at last, we proposed a novel clustering algorithm which used both the linguistics knowledge and the statistical knowledge. We built a coreference resolution sys...

Training machine learning algorithms for land cover classification is labour intensive. Applying active learning strategies tries to alleviate this, but can lead to unexpected results. We demonstrate what can go wrong when uncertainty sampling with an SVM is applied to real world remote sensing data. Possible causes and solutions are suggested.

We study pool-based active learning in the presence of noise, that is, the agnostic setting. It is known that the effectiveness of agnostic active learning depends on the learning problem and the hypothesis space. Although there are many cases on which active learning is very useful, it is also easy to construct examples that no active learning algorithm can have an advantage. Previous works ha...

Researchers have debated whether instructional-based teaching or exploration-based active learning is better for decades with unsatisfying results. A main obstacle is the difficulty in precisely controlling and characterizing the pedagogical methods used and the learning conditions in empirical studies. To address this, we leveraged existing computational models of teaching and active learning ...