3096549
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
619505
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product databases. Such a representation is useful for a variety of tasks where treating a product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include product recommendations, product comparison, and demand forecasting. We formulate the extraction as a classification problem and use a semi-supervised algorithm (co-EM) along with (Naive Bayes). The extraction system requires very little initial user supervision: using unlabeled data, we automatically extract an initial seed list that serves as training data for the supervised and semi-supervised classification algorithms. Finally, the extracted attributes and values are linked to form pairs using dependency information and co-location scores. We present promising results on product descriptions in two categories of sporting goods.
2508978
Gait Sequence Analysis Using Frieze Patterns
We analyze walking people using a gait sequence representation that bypasses the need for frame-to-frame tracking of body parts. The gait representation maps a video sequence of silhouettes into a pair of two-dimensional spatio-temporal patterns that are near-periodic along the time axis. Mathematically, such patterns are called "frieze" patterns and associated symmetry groups "frieze groups". With the help of a walking humanoid avatar, we explore variation in gait frieze patterns with respect to viewing angle, and find that the frieze groups of the gait patterns and their canonical tiles enable us to estimate viewing direction of human walking videos. In addition, analysis of periodic patterns allows us to determine the dynamic time warping and affine scaling that aligns two gait sequences from similar viewpoints. We also show how gait alignment can be used to perform human identification and model-based body part segmentation.
37864683
Genetic programming model for software quality classification
We apply genetic programming techniques to build a software quality classification model based on the metrics of software modules. The model we built attempts to distinguish the fault-prone modules from non-fault-prone modules using genetic programming (GP). These GP experiments were conducted with a random subset selection for GP in order to avoid overfitting. We then use the whole fit data set as the validation data set to select the best model. We demonstrate through two case studies that the GP technique can achieve good results. Also, we compared GP modeling with logistic regression modeling to verify the usefulness of GP.
36759557
Geometrical analysis of two sets of 3D correspondence data patterns
Given two sets of image correspondence data, the analysis of the corresponding rigid body transformation that accurately describes the object's motion in 3D space is a fundamental problem in image understanding. While many methods have been put forward to analyse 3D transformations, such methods do not make use of the vector distance between feature points and angular information as constraints to analyse transformation parameters. Current methods and algorithms have also suffered from the problems of lack of efficiency, sensitivity to noise, and multiplicity of solutions. In this paper we present a novel geometrical analysis of rigid body transformations and a novel algorithm to calibrate transformation parameters based on image correspondence. The algorithm is validated through experimental calibration and a comparison is made with calibration implemented by the least squares method. We demonstrate that the proposed algorithm works well in the presence of noise and that its performance is in general superior to algorithms based on the least squares method.
15998897
Incrementalization across object abstraction
Object abstraction supports the separation of what operations are provided by systems and components from how the operations are implemented, and is essential in enabling the construction of complex systems from components. Unfortunately, clear and modular implementations have poor performance when expensive query operations are repeated, while efficient implementations that incrementally maintain these query results are much more difficult to develop and to understand, because the code blows up significantly, and is no longer clear or modular.This paper describes a powerful and systematic method that first allows the "what" of each component to be specified in a clear and modular fashion and implemented straightforwardly in an object-oriented language; then analyzes the queries and updates, across object abstraction, in the straightforward implementation; and finally derives the sophisticated and efficient "how" of each component by incrementally maintaining the results of repeated expensive queries with respect to updates to their parameters. Our implementation and experimental results for example applications in query optimization, role-based access control, etc. demonstrate the effectiveness and benefit of the method.
9238814
Building Efficient Overlays
Abstract
Peer-to-peer (P2P) and Grid computing systems have emerged as popular models aiming at further utilizing Internet information and resources, complementing the available client–server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch problems between the overlay network and the physical underlying network. The topology mismatch problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques in P2P and Grid systems. Aiming at alleviating the mismatch problem and reducing the unnecessary traffic, we have proposed two approaches, adaptive overlay topology optimization (AOTO) and location-aware topology matching (LTM) techniques, to reduce the total traffic cost and average query response time. Both AOTO and LTM are scalable and completely distributed in the sense that they do not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. This paper shows the effectiveness of AOTO and LTM and compares the performance of these two approaches through simulation studies.
