8040923
Inductive Policy Selection for First-Order MDPs
We select policies for large Markov Decision Processes (MDPs) with compact first-order representations. We find policies that generalize well as the number of objects in the domain grows, potentially without bound. Existing dynamic-programming approaches based on flat, propositional, or first-order representations either are impractical here or do not naturally scale as the number of objects grows without bound. We implement and evaluate an alternative approach that induces first-order policies using training data constructed by solving small problem instances using PGraphplan (Blurn & Langford, 1999). Our policies are represented as ensembles of decision lists, using a taxonomic concept language. This approach extends the work of Martin and Geffner (2000) to stochastic domains, ensemble learning, and a wider variety of problems. Empirically, we find "good" policies for several stochastic first-order MDPs that are beyond the scope of previous approaches. We also discuss the application of this work to the relational reinforcement-learning problem.
2770616
Learning Domain-Specific Control Knowledge from Random Walks
We describe and evaluate a system for learning domain-specific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well on "long random walk" problem distributions. The system is based on viewing planning domains as very large Markov decision processes and then applying a recent variant of approximate policy iteration that is bootstrapped with a new technique based on random walks. We evaluate the system on the AIPS-2000 planning domains (among others) and show that often the learned policies perform well on problems drawn from the long–random-walk distribution. In addition, we show that these policies often perform well on the original problem distributions from the domains involved. Our evaluation also uncovers limitations of our current system that point to future challenges.
477360
Approximate Policy Iteration with a Policy Language Bias
We explore approximate policy iteration, replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.
6736183
Feature selection based on maximizing separability in Gauss mixture model and its application to image classification
We propose a feature selection algorithm suitable for classification problems. Our algorithm tries to find a subset of features, which maximizes separability between Gaussian clusters. To reduce the complexity of exhaustive searching the best feature set, we follow a backward elimination method. Our feature selection algorithm can be applied to a full search classifier to obtain a single global subspace. However, one global subspace may not alone capture local behavior well. We realize multiple subspace clustering by applying our dimension reduction algorithm to a tree structured classifier. Experimental results show that the resulting classifier not only removes irrelevant features but also improves classification performance.
14200816
Context-dependent tree-structured image classification using the QDA distortion measure and the hidden Markov model
Vector quantization based on the Gauss mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further, the pruning stage of our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, our algorithm aims to iteratively maximize the joint probability of occurrence of all image blocks based on the HMM. Our simulation results indicate that our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously published algorithms based on the GMM.
7105370
Realistic human hand deformation
We present a new approach to realistic hand modeling and deformation with real-time performance. We model the underlying shape of a human hand by means of sweeps which follow a simplified skeleton. The resulting swept surfaces are blended, and an auxiliary surface is then bound to the swept representation in the palm region. In the areas of this palm-control surface where bulges occur in certain poses of a real hand, the vertices are given their own trajectories, so that the palm forms realistic shapes as the joints bend. Palm lines can also be modeled as valleys in the skin by sketching them on a displacement map on the palm-control surface, and activitating them when appropriate joint movements take place. Self-intersections and collisions are detected using geometric primitives that are automatically generated from, and deform with, the sweeps and palm surface. Our algorithm runs in real time, and the naturalism of its results are demonstrated by comparative images of modeled and real hands, including several challenging poses. Copyright © 2006 John Wiley & Sons, Ltd.
3134846
Tiny-sync: Tight time synchronization for wireless sensor networks
Time synchronization is a fundamental middleware service for any distributed system. Wireless sensor networks make extensive use of synchronized time in many contexts (e.g., data fusion, TDMA schedules, synchronized sleep periods, etc.). We propose a time synchronization method relevant for wireless sensor networks. The solution features minimal complexity in network bandwidth, storage as well as processing, and can achieve good accuracy. Especially relevant for sensor networks, it also provides tight, deterministic bounds on offset and clock drift. A method for synchronizing the entire network is presented. The performance of the algorithm is analyzed theoretically and validated on a realistic testbed. The results show that the proposed algorithm outperforms existing algorithms in terms of precision and resource requirements.
347760
Automatic Skin Pixel Selection and Skin Color Classification
We describe an automatic method for classifying skin color, independent of lighting and imaging device characteristics, using consumer digital cameras and a simple color calibration target. After color normalization and face detection is performed, pixels of each face image are clustered in an unsupervised fashion. Pixels likely to be representative of skin color, rather than of distractors such as shadows, specularities, eyes, and lips, are identified by selecting the dominant clusters that have large number of pixels assigned per volume. A Gauss mixture model (GMM) of a person's skin color is formed from the pixels belonging to the selected clusters. When a set of exemplar images with skin color labels by an expert, we show that the label assigned by the same expert to a new, test face image can be predicted by comparison of the GMMs of the test image and the exemplars. Specifically, we use the label of the exemplar whose GMM has smallest KL divergence from that of the test image.