14320102
Efficient Motion Planning Based on Disassembly
Disassembly-based motion planning (DBMP) is a novel and efficient single-query, sampling-based motion planning approach for free-flying robots. Disassembly-based motion planning uses workspace information to determine the workspace volume of a potential solution path and uses this information to exclude large portions of configuration space from exploration. It also identifies the most constrained placements of the robot along the potential solution path. These placements are referred to as assemblies because they are highly constrained by the environment, much like parts in an assembly are constrained. The constraints limit the possible motions of the robot and thus can be exploited to further limit configuration space exploration. The use of these two sources of workspace information permits the solution of many practical problems with very limited configuration space exploration. This reduction in configuration space exploration results in performance improvements of several orders of magnitude, compared to state-of-the-art motion planning methods. For non-free-flying robots, disassembly-based motion planning performs at least as well as the sampling-based motion planning method it is based on.
4829760
Exploiting redundancy to implement multiobjective behavior
Teams of robots can be redundant with respect to a given task. This redundancy can be exploited to pursue additional objectives during the execution of the task. In this paper, we describe a control-based method to exploit such redundancy for the execution of additional behavior, leading to the improvement of overall performance. The control-based method provides a suitable mechanism for combining controllers with different objectives. The mechanism ensures that the subordinate controllers do not interfere with the superior controllers. Thus it allows to build controllers exhibiting complex behavior from simple primitives, while maintaining their provable performance characteristics. The effectiveness of the framework is demonstrated by experiments with a multi-robot exploration task.
10225272
Ensemble Selection for SuperParent-One-Dependence Estimators
SuperParent-One-Dependence Estimators (SPODEs) loosen Naive-Bayes’ attribute independence assumption by allowing each attribute to depend on a common single attribute (superparent) in addition to the class. An ensemble of SPODEs is able to achieve high classification accuracy with modest computational cost. This paper investigates how to select SPODEs for ensembling. Various popular model selection strategies are presented. Their learning efficacy and efficiency are theoretically analyzed and empirically verified. Accordingly, guidelines are investigated for choosing between selection criteria in differing contexts.
156436
Learning Multiple Related Tasks using Latent Independent Component Analysis
We propose a probabilistic model based on Independent Component Analysis for learning multiple related tasks. In our model the task parameters are assumed to be generated from independent sources which account for the relatedness of the tasks. We use Laplace distributions to model hidden sources which makes it possible to identify the hidden, independent components instead of just modeling correlations. Furthermore, our model enjoys a sparsity property which makes it both parsimonious and robust. We also propose efficient algorithms for both empirical Bayes method and point estimation. Our experimental results on two multi-label text classification data sets show that the proposed approach is promising.
15796925
Combining Multiple Learning Strategies for Effective Cross Validation
Parameter tuning through cross-validation becomes very diicult when the validation set contains no or only a few examples of the classes in the evaluation set. We address this open challenge by using a combination of classiiers with diierent performance characteristics to eeectively reduce the performance variance on average of the overall system across all classes, including those not seen before. This approach allows us to tune the combination system on available but less-representative validation data and obtain smaller performance degradation of this system on the evaluation data than using a single-method classiier alone. We tested this approach by applying k-Nearest Neighbor, Rocchio and Language Modeling classiiers and their combination to the event tracking problem in the Topic Detection and Tracking (TDT) domain, where new classes (events) are created constantly over time, and representative validation sets for new classes are often diicult to obtain on time. When parameters tuned on an early benchmark TDT corpus were evaluated on a later TDT benchmark corpus with no overlapping events, we observed a 38-65% reduction in tracking cost (a weighted combination of errors) by the combined system over the individual methods evaluated under the same conditions, strongly suggesting the robustness of this approach as a solution for improving cross-class performance consistency of statistical classi-ers when standard cross-validation fails due to the lack of representative validation sets.
15157984
Image morphing based on mutual information and optimal mass transport
Time domain image interpolation, or image morphing, refers to a class of techniques for generating a series of smoothly changing intermediate images between two given related images. In this note, we present a novel approach based on the theoiy of optimal mass transport, using mutual information (MI) as the similarity measurement. The potential applications also include image registmtion, compression and coding.
11919242
Detecting and Classifying Emotion in Popular Music
Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.