Many practitioners currently use rules of thumb to price tasks on online labor markets. Incorrect pricing leads to task starvation or inefficient use of capital. Formal pricing policies can address these challenges. In this paper we argue that a pricing policy can be based on the trade-off between price and desired completion time.We show how this duality can lead to a better pricing policy for tasks in online labor markets. This paper makes three contributions. First, we devise an algorithm for job pricing using a survival analysis model. We then show that worker arrivals can be modeled as a non-homogeneous Poisson Process (NHPP). Finally using NHPP for worker arrivals and discrete choice models we present an abstract mathematical model that captures the dynamics of the market when full market information is presented to the task requester. This model can be used to predict completion times and pricing policies for both public and private crowds.

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions. We further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient. We also extend these results to active learning in the presence of label noise, and find that even under broad classes of noise distributions, we can typically guarantee strict improvements over the known results for passive learning.

This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.

Multi-task learning aims at improving the generalization performance of a learning task with the help of some other related tasks. Although many multi-task learning methods have been proposed, they are all based on the assumption that all tasks share the same data representation. This assumption is too restrictive for general applications. In this paper, we propose a multi-task extension of linear discriminant analysis (LDA), called multi-task discriminant analysis (MTDA), which can deal with learning tasks with different data representations. For each task, MTDA learns a separate transformation which consists of two parts, one specific to the task and one common to all tasks. A by-product of MTDA is that it can alleviate the labeled data deficiency problem of LDA. Moreover, unlike many existing multi-task learning methods, MTDA can handle binary and multi-class problems for each task in a generic way. Experimental results on face recognition show that MTDA consistently outperforms related methods.

In this paper, we propose a novel method to select the most informativesubset of features, which has little redundancy andvery strong discriminating power. Our proposed approach automaticallydetermines the optimal number of features and selectsthe best subset accordingly by maximizing the averagepairwise informativeness, thus has obvious advantage overtraditional filter methods. By relaxing the essential combinatorialoptimization problem into the standard quadratic programmingproblem, the most informative feature subset canbe obtained efficiently, and a strategy to dynamically computethe redundancy between feature pairs further greatly acceleratesour method through avoiding unnecessary computationsof mutual information. As shown by the extensive experiments,the proposed method can successfully select the mostinformative subset of features, and the obtained classificationresults significantly outperform the state-of-the-art results onmost test datasets.

Video games are increasingly producing huge datasets available for analysis resulting from players engaging in interactive environments. These datasets enable investigation of individual player behavior at a massive scale, which can lead to reduced production costs and improved player retention. We present an approach for modeling player retention in Madden NFL 11, a commercial football game. Our approach encodes gameplay patterns of specific players as feature vectors and models player retention as a regression problem. By building an accurate model of player retention, we are able to identify which gameplay elements are most influential in maintaining active players. The outcome of our tool is recommendations which will be used to influence the design of future titles in the Madden NFL series.

NewsFinder automates the steps involved in finding, selecting and publishing news stories that meet subjective judgments of relevance and interest to the Artificial Intelligence community. NewsFinder combines a broad search with AI-specific filters and incorporates a learning program whose judgment of interestingness of stories can be trained by feedback from readers. Since August, 2010, the program has been used to operate the AI in the News service that is part of the AAAI AITopics site.

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task and shares difficulties encountered in transitioning AI technology from university researchers to patients and physicians.

Clustering is a fundamental research topic in the field of data mining. Optimizing the objective functions of clustering algorithms, e.g. normalized cut and k-means, is an NP-hard optimization problem. Existing algorithms usually relax the elements of cluster indicator matrix from discrete values to continuous ones. Eigenvalue decomposition is then performed to obtain a relaxed continuous solution, which must be discretized. The main problem is that the signs of the relaxed continuous solution are mixed. Such results may deviate severely from the true solution, making it a nontrivial task to get the cluster labels. To address the problem, we impose an explicit nonnegative constraint for a more accurate solution during the relaxation. Besides, we additionally introduce a discriminative regularization into the objective to avoid overfitting. A new iterative approach is proposed to optimize the objective. We show that the algorithm is a general one which naturally leads to other extensions. Experiments demonstrate the effectiveness of our algorithm.

Manifold learning is a powerful tool for reducing the dimensionality of a dataset by finding a low-dimensional embedding that retains important geometric and topological features. In many applications it is desirable to add new samples to a previously learnt embedding, this process of adding new samples is known as the out-of-sample extension problem. Since many manifold learning algorithms do not naturally allow for new samples to be added we present an easy to implement generalized solution to the problem that can be used with any existing manifold learning algorithm. Our algorithm is based on simple geometric intuition about the local structure of a manifold and our results show that it can be effectively used to add new samples to a previously learnt embedding. We test our algorithm on both artificial and real world image data and show that our method significantly out performs existing out-of-sample extension strategies.