We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.

Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably lower than existing approaches, so the processing can be done in real time on a low-powered portable device such as a mobile phone. We evaluate the performance of our algorithm on a large, noisy data set comprising over 50 hours of data from six different subjects, including activities such as running and walking up or down stairs. We also demonstrate the ability of the system to accurately classify an individual from a set of 25 people, based only on the characteristics of their walking gait. The system requires very little parameter tuning, and can be trained with small amounts of data.

No abstract available.

Learning agents often find themselves in environments in which rare significant events occur independently of their current choice of action. Traditional reinforcement learning algorithms sample events according to their natural probability of occurring, and therefore tend to exhibit slow convergence and high variance in such environments. In this thesis, we assume that learning is done in a simulated environment in which the probability of these rare events can be artificially altered. We present novel algorithms for both policy evaluation and control, using both tabular and function approximation representations of the value function. These algorithms automatically tune the rare event probabilities to minimize the variance and use importance sampling to correct for changes in the dynamics. We prove that these algorithms converge, provide an analysis of their bias and variance, and demonstrate their utility in a number of domains, including a large network planning task.