8746722
Parameter space exploration with Gaussian process trees
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are unnecessary in regions where the response is easy predicted; well-chosen designs could allow a mapping of the response with far fewer simulation runs. Thus, there is a need for computationally inexpensive surrogate models and an accompanying method for selecting small designs. We explore a general methodology for addressing this need that uses non-stationary Gaussian processes. Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling. Our methods are illustrated on several examples, including a motivating example involving computational fluid dynamics simulation of a NASA reentry vehicle.
1526029
Lossless Online Bayesian Bagging
Bagging frequently improves the predictive performance of a model. An online version has recently been introduced, which attempts to gain the benefits of an online algorithm while approximating regular bagging. However, regular online bagging is an approximation to its batch counterpart and so is not lossless with respect to the bagging operation. By operating under the Bayesian paradigm, we introduce an online Bayesian version of bagging which is exactly equivalent to the batch Bayesian version, and thus when combined with a lossless learning algorithm gives a completely lossless online bagging algorithm. We also note that the Bayesian formulation resolves a theoretical problem with bagging, produces less variability in its estimates, and can improve predictive performance for smaller data sets.
34417408
Knowledge discovery and data mining
This special issue contains selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held in Singapore on 2223 February 1997. PAKDD is one of the few conference series dedicated to knowledge discovery and data mining. The PAKDD seeks to provide a forum for researchers and practitioners from the Pacific Asia region as well as world-wide in KDD research and development. The data explosion, brought about by the pervasive use of computers, has caused many organizations to lament, "'Computers have promised us a fountain of wisdom but delivered a flood of data." The advent of KDD has provided data analysis tools to cope with the massive databases that have ensued. The scientific challenges for KDD, coupled with a practical orientation, have ignited the interest of both the academic and user communities. This, in turn, has provided the motivation to create PAKDD for the tyro communities to interact. PAKDD'97 is the first conference in this series. This inaugural conference was very well received, One hundred and twenty researchers and practitioners from all over the world attended the conference. From the twenty-six papers presented in its technical sessions, seven have been selected fl)r publication in this special issue. Reflecting the diversity of research work, these papers address different issues in the area of knowledge discovery and data mining. The goal of data mining is to discover knowledge from data. However, most mining processes usually require certain domain knowledge. Such requirements often make a discovery system impractical or too restricted. On the other hand, adequate a priori knowledge can make the process of discovery more effective. Finding the equilibrium is indeed an art. The first paper in our selection, "'Discovery of First Principle Equations Based on Scale-Type-Based and DataDriven Reasoning", co-authored by T. Washio and H. Motoda, gives such an example. The authors propose a new approach that enables automatic discovery of formulae of first principles from the measurement data with minimum knowledge of quantity scale-types. Classification is one of the well-studied data mining problems. Most classification techniques stem from machine learning. One limitation of these popular
7431488
An HMM-Based Threshold Model Approach for Gesture Recognition
A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14% reliability.
46208894
Evaluation of Feature Extraction Techniques for Robust Watermarking
This paper addresses feature extraction techniques for robust watermarking. Geometric distortion attacks desynchronize the location of the inserted watermark and hence prevent watermark detection. Watermark synchronization, which is a process of finding the location for watermark insertion and detection, is crucial to design robust watermarking. One solution is to use image features. This paper reviews feature extraction techniques that have been used in featurebased watermarking: the Harris corner detector and the Mexican Hat wavelet scale interaction method. We also evaluate the scale-invariant keypoint extractor in comparison with other techniques in aspect of watermarking. After feature extraction, the set of triangles is generated by Delaunay tessellation. These triangles are the location for watermark insertion and detection. Redetection ratio of triangles is evaluated against geometric distortion attacks as well as signal processing attacks. Experimental results show that the scale-invariant keypoint extractor is appropriate for robust watermarking.
10061799
SEEMLESS: distributed algorithm for topology control of survivable energy efficient multihop wireless sensor networks using adjustable transmission power
This paper suggests a distributed algorithm named (SEEMLESS) survivable energy efficient multihop wireless sensor networks which determines a transmission power level for each sensor in a wireless sensor network (WSN) with guaranteeing survivability of WSN topology and minimizing the energy consumption of each sensor. The SEEMLESS algorithm provides a mechanism to make WSN topology survivable by letting each sensor have two closest neighbors and by letting each cluster head have one additional neighbor.
15989010
Interactive Object-Based Retrieval Using Relevance Feedback
In this paper we present an interactive, object-based video retrieval system which features a novel query formulation method that is used to iteratively refine an underlying model of the search object. As the user continues query composition and browsing of retrieval results, the system’s object modeling process, based on Gaussian probability distributions, becomes incrementally more accurate, leading to better search results. To make the interactive process understandable and easy to use, a custom user-interface has been designed and implemented that allows the user to interact with segmented objects in formulating a query, in browsing a search result, and in re-formulating a query by selecting an object in the search result.