Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.

We call this task the rating-inference task; Traditional sentiment analysis mainly considers It determines an author's polarity evaluation within a multipoint binary classifications of reviews, but in many scale (e.g. one to five "stars"). We explore solutions for real-world sentiment classification problems, nonbinary this task in the context of product or service reviews, which review ratings are more useful. This is especially are one of the most important opinion resources and widely true when consumers wish to compare two used by costumers and companies. We observe that in many products, both of which are not negative. Previous real-world scenarios, it is important to provide numerical ratings work has addressed this problem by extracting rather than binary decisions, especially when a customer various features from the review text for learning a compares several candidate products, all of them are positive predictor. Since the same word may have different in a binary classification, to make a purchase decision, since sentiment effects when used by different reviewers customers not only need to know whether a product is good or on different products, we argue that it is necessary not, but also how good the product is. A recent study pointed to model such reviewer and product dependent effects out that many consumers are willing to pay at least 20% percent in order to predict review ratings more accurately.

This paper identifies a widely existing phenomenon in web data, which we call the "words of few mouths" phenomenon. This phenomenon, in the context of online reviews, refers to the case that a large fraction of the reviews are each voted only by very few users. We discuss the challenges of "words of few mouths" in the development of recommender systems based on users' opinions and advocate probabilistic methodologies to handle such challenges. We develop a probabilistic model and correspondingly a logistic regression based learning algorithm for review helpfulness prediction. Our experimental results indicate that the proposed model outperforms the current state-of-the-art algorithms not only in the presence of the "words of few mouths" phenomenon, but also in the absence of such phenomena.

Data sets containing multi-manifold structures are ubiquitous in real-world tasks, and effective grouping of such data is an important yet challenging problem. Though there were many studies on this problem, it is not clear on how to design principled methods for the grouping of multiple hybrid manifolds. In this paper, we show that spectral methods are potentially helpful for hybridmanifold clustering when the neighborhood graph is constructed to connect the neighboring samples from the same manifold. However, traditional algorithms which identify neighbors according to Euclidean distance will easily connect samples belonging to different manifolds. To handle this drawback, we propose a new criterion, i.e., local and structural consistency criterion, which considers the neighboring information as well as the structural information implied by the samples. Based on this criterion, we develop a simple yet effective algorithm, named Local and Structural Consistency (LSC), for clustering with multiple hybrid manifolds. Experiments show that LSC achieves promising performance.

We consider the problem of learning to act in partially observable, continuous-state-and-action worlds where we have abstract prior knowledge about the structure of the optimal policy in the form of a distribution over policies. Using ideas from planning-as-inference reductions and Bayesian unsupervised learning, we cast Markov Chain Monte Carlo as a stochastic, hill-climbing policy search algorithm. Importantly, this algorithm's search bias is directly tied to the prior and its MCMC proposal kernels, which means we can draw on the full Bayesian toolbox to express the search bias, including nonparametric priors and structured, recursive processes like grammars over action sequences. Furthermore, we can reason about uncertainty in the search bias itself by constructing a hierarchical prior and reasoning about latent variables that determine the abstract structure of the policy. This yields an adaptive search algorithm---our algorithm learns to learn a structured policy efficiently. We show how inference over the latent variables in these policy priors enables intra- and intertask transfer of abstract knowledge. We demonstrate the flexibility of this approach by learning meta search biases, by constructing a nonparametric finite state controller to model memory, by discovering motor primitives using a simple grammar over primitive actions, and by combining all three.

Combining graph regularization with nonnegative matrix (tri-)factorization (NMF) has shown great performance improvement compared with traditional nonnegative matrix (tri-)factorization models due to its ability to utilize the geometric structure of the documents and words. In this paper, we show that these models are not well-defined and suffering from trivial solution and scale transfer problems. In order to solve these common problems, we propose two models for graph regularized nonnegative matrix (tri-)factorization, which can be applied for document clustering and co-clustering respectively. In the proposed models, a Normalized Cut-like constraint is imposed on the cluster assignment matrix to make the optimization problem well-defined. We derive a multiplicative updating algorithm for the proposed models, and prove its convergence. Experiments of clustering and co-clustering on benchmark text data sets demonstratethat the proposed models outperform the originalmodels as well as many other state-of-the-art clustering methods.

In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.

Linear classification is a useful tool for dealing with large-scale data in applications such as document classification and natural language processing. Recent developments of linear classification have shown that the training process can be efficiently conducted. However, when the data size exceeds the memory capacity, most training methods suffer from very slow convergence due to the severe disk swapping. Although some methods have attempted to handle such a situation, they are usually too complicated to support some important functions such as parameter selection. In this paper, we introduce a block minimization framework for data larger than memory. Under the framework, a solver splits data into blocks and stores them into separate files. Then, at each time, the solver trains a data block loaded from disk. Although the framework is simple, the experimental results show that it effectively handles a data set 20 times larger than the memory capacity.

Entity linking maps name mentions in the documents to entries in a knowledge base through resolving the name variations and ambiguities. In this paper, we propose three advancements for entity linking. Firstly, expanding acronyms can effectively reduce the ambiguity of the acronym mentions. However, only rule-based approaches relying heavily on the presence of text markers have been used for entity linking. In this paper, we propose a supervised learning algorithm to expand more complicated acronyms encountered, which leads to 15.1% accuracy improvement over state-of-the-art acronym expansion methods. Secondly, as entity linking annotation is expensive and labor intensive, to automate the annotation process without compromise of accuracy, we propose an instance selection strategy to effectively utilize the automatically generated annotation. In our selection strategy, an informative and diverse set of instances are selected for effective disambiguation. Lastly, topic modeling is used to model the semantic topics of the articles. These advancements give statistical significant improvement to entity linking individually. Collectively they lead the highest performance on KBP-2010 task.

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. We apply non-linear regression within Monte-Carlo search, online, to estimate a state-action value function from the outcomes of random roll-outs. This value function generalizes between related states and actions, and can therefore provide more accurate evaluations after fewer rollouts. A further significant advantage of this approach is its ability to automatically extract and leverage domain knowledge from external sources such as game manuals. We apply our algorithm to the game of Civilization II, a challenging multi-agent strategy game with an enormous state space and around 10^21 joint actions. We approximate the value function by a neural network, augmented by linguistic knowledge that is extracted automatically from the official game manual. We show that this non-linear value function is significantly more efficient than a linear value function, which is itself more efficient than Monte-Carlo tree search. Our non-linear Monte-Carlo search wins over 78% of games against the built-in AI of Civilization II.