Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such non-direct work and describes the development of tools to characterize conflict and coordination costs in Wikipedia. The results may inform the design of new collaborative knowledge systems. Author Keywords Wikipedia, wiki, collaboration, conflict, user model, Web-based interaction, visualization. ACM Classification Keywords

| In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight distribution should be taken into account upon prediction, and it also alleviates the usual tendency of assigning overly high condence to the estimated class memberships of the test patterns. Moreover, the moderated output derived here can be taken as an approximation to the posterior class probability. Hence, meaningful rejection thresholds can be assigned and outputs from several networks can be directly compared. Experimental results on both articial and real-world data are also discussed. Keywords|Support vector machine, Evidence framework, Moderated output, Bayesian I. Introduction I N recent years, there has been a lot of interest in studying the support vector machine (SVM) [1], [2], [3], [4], [5], [6], [7]. SVM is based on the i...

We present the maximum entropy discrimination (MED) formalism as a regularization approach with information theoretic penalties. By extending discriminative and large margin concepts to a probabilistic setting, MED permits many important generalizations to SVMs. We introduce feature selection as a particularly critical augmentation of the learning machine. MED derivations for both regression and classification cases are shown and lead to promising experimental results. Features are pruned simultaneously with parameter estimation to generate substantial improvements with relatively sparse training data. Furthermore, in the linear model case, complexity scales linearly with dimensionality and can remain tractable under explicit feature expansions of non-linear kernels. The MED formalism also accommodates discriminant functions that arise from generative probability models (log-likelihood ratios) although feature selection may require more computational effort and ap...

Abstract — Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non- smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp. I.

I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This can provide intuitive guidelines for choosing a `good' SVM kernel. It can also assign (by evidence maximization) optimal values to parameters such as the noise level C which cannot be determined unambiguously from properties of the MAP solution alone (such as cross-validation error) . I illustrate this using a simple approximate expression for the SVM evidence. Once C has been determined, error bars on SVM predictions can also be obtained. 1 Support Vector Machines: A probabilistic framework Support Vector Machines (SVMs) have recently been the subject of intense research activity within the neural networks community; for tutorial introductions and overviews of recent developments see [1, 2, 3]. One of the open questions that remains is how to set the `tunable' parameters of an SVM algorithm: While methods for...

We give necessary and sufficient conditions for uniqueness of the support vector solution for the problems of pattern recognition and regression estimation, for a general class of cost functions. We show that if the solution is not unique, all support vectors are necessarily at bound, and we give some simple examples of non-unique solutions. We note that uniqueness of the primal (dual) solution does not necessarily imply uniqueness of the dual (primal) solution. We show how to compute the threshold b when the solution is unique, but when all support vectors are at bound, in which case the usual method for determining b does not work. 1 Introduction Support vector machines (SVMs) have attracted wide interest as a means to implement structural risk minimization for the problems of classification and regression estimation. The fact that training an SVM amounts to solving a convex quadratic programming problem means that the solution found is global, and that if it is not unique...

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars.

Abstract — Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver’s field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity- and lighting-invariant system to estimate a driver’s head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver’s head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system. I.

This paper describes a method for creating an evaluation measure for discourse understanding in spoken dialogue systems. No well-established measure has yet been proposed for evaluating discourse understanding, which has made it necessary to evaluate it only on the basis of the system’s total performance. Such evaluations, however, are greatly influenced by task domains and dialogue strategies. To find a measure that enables good estimation of system performance only from discourse understanding results, we enumerated possible discourse-understanding-related metrics and calculated their correlation with the system’s total performance through dialogue experiments.

The goal of active learning is to minimize the cost of training an accurate model by allowing the learner to choose which instances are labeled for training. However, most research in active learning to date has assumed that the cost of acquiring labels is the same for all instances. In domains where labeling costs may vary, a reduction in the number of labeled instances does not guarantee a reduction in cost. To better understand the nature of actual labeling costs in such domains, we present a detailed empirical study of active learning with annotation costs in four real-world domains involving human annotators. 1