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There has been much recent attention to the problem of learning an appropriate distance metric, using class labels or other side information. Some proposed algorithms are iterative and computationally expensive. In this paper, we show how to solve one ...

Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity--the number ...

Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality reduction framework. However, these algorithms yield projective maps which ...

In this paper, we present a general data clustering algorithm which is based on the asymmetric pairwise measure of Markov random walk hitting time on directed graphs. Unlike traditional graph based clustering methods, we do not explicitly calculate the ...

This paper addresses the question of how statistical learning algorithms can be integrated into a larger AI system both from a practical engineering perspective and from the perspective of correct representation, learning, and reasoning. Our goal is to ...

An important feature of many problem domains in machine learning is their geometry. For example, adjacency relationships, symmetries, and Cartesian coordinates are essential to any complete description of board games, visual recognition, or vehicle ...

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ...

Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL ...

We introduce the problem of zero-data learning, where a model must generalize to classes or tasks for which no training data are available and only a description of the classes or tasks are provided. Zero-data learning is useful for problems where the ...

With the success of local features in object recognition, feature-set representations are widely used in computer vision and related domains. Pyramid match kernel (PMK) is an efficient approach to quantifying the similarity between two unordered feature-...

Complex graphs, in which multi-type nodes are linked to each other, frequently arise in many important applications, such as Web mining, information retrieval, bioinformatics, and epidemiology. In this study, We propose a general framework for ...

This paper presents a fast simulated annealing framework for combining multiple clusterings (i.e. clustering ensemble) based on some measures of agreement between partitions, which are originally used to compare two clusterings (the obtained clustering ...

Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a feature subset is selected based on the corresponding score (subset-level ...

Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this ...

For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, commonly referred to as a pipeline model. Typically, such scenarios are ...

Hierarchical state decompositions address the curse-of-dimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, we introduce the Economic Hierarchical Q-Learning (EHQ) algorithm for ...

Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery approach for support vector machines and propose to discover Markov ...

Traditional artificial intelligence techniques do not perform well in applications such as real-time strategy games because of the extensive search spaces which need to be explored. In addition, this exploration must be carried out on-line during ...

ADtrees, a data structure useful for caching sufficient statistics, have been successfully adapted to grow lazily when memory is limited and to update sequentially with an incrementally updated dataset. For low arity symbolic features, ADtrees trade a ...

This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages--traditional STRIPS, and a new language STRIPS+WS that extends STRIPS to ...

The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms is to require that the predicted data labels should be sufficiently smooth with respect to the intrinsic data manifold. In ...

In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local regularization part, our algorithm constructs a regularized classifier for each data point using its neighborhood, while the ...

In this paper, we present a novel probabilistic framework for recovering global, latent social network structure from local, noisy observations. We extend curved exponential random graph models to include two types of variables: hidden variables that ...

We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external class dynamics to label sequence data. We introduce a small number of ...

Classification is one of the most fundamental problems in machine learning, which aims to separate the data from different classes as far away as possible. A common way to get a good classification function is to minimize its empirical prediction loss ...

The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for ...

It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through resampling the instances or features. In this paper, we propose an ...

To-do lists have been found to be the most popular personal information management tools, yet there is no automated system to interpret and act upon them when appropriate on behalf of the user. Automating to-do lists is challenging, not only because ...

Machine learning systems are deployed in many adversarial conditions like intrusion detection, where a classifier has to decide whether a sequence of actions come from a legitimate user or not. However, the attacker, being an adversarial agent, could ...

This paper debuts a novel application of speech recognition to foreign language learning. We present a generic framework for developing user-customizable card games designed to aid learners in the difficult task of vocabulary acquisition. We also ...