This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a sampling approach to estimating the expected reduction in error due to the labeling of a query. In experimental results on two real-world data sets we reach high accuracy very quickly, sometimes with four times fewer labeled examples than competing methods. 1.

When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it di#cult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone.

Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision. 1

In many cost-sensitive environments class probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class probability estimates; however, it often is very costly to obtain training data with class labels. Active learning acquires data incrementally, at each phase identifying especially useful additional data for labeling, and can be used to economize on examples needed for learning. We outline the critical features of an active learner and present a sampling-based active learning method for estimating class probabilities and class-based rankings. BOOT- STRAP-LV identifies particularly informative new data for learning based on the variance in probability estimates, and uses weighted sampling to account for a potential example's informative value for the rest of the input space. We show empirically that the method reduces the number of data items that must be obtained and labeled, across a wide variety of domains. We investigate the contribution of the components of the algorithm and show that each provides valuable information to help identify informative examples. We also compare BOOTSTRAP- LV with UNCERTAINTY SAMPLING, an existing active learning method designed to maximize classification accuracy. The results show that BOOTSTRAP-LV uses fewer examples to exhibit a certain estimation accuracy and provide insights to the behavior of the algorithms. Finally, we experiment with another new active sampling algorithm drawing from both UNCERTAINTY SAMPLING and BOOTSTRAP-LV and show that it is significantly more competitive with BOOTSTRAP-LV compared to UNCERTAINTY SAMPLING. The analysis suggests more general implications for improving existing active sampling ...

Query by Committee is an eective approach to selective sampling in which disagreement amongst an ensemble of hypotheses is used to select data for labeling. Query by Bagging and Query by Boosting are two practical implementations of this approach that use Bagging and Boosting, respectively, to build the committees. For eective active learning, it is critical that the committee be made up of consistent hypotheses that are very dierent from each other. Decorate is a recently developed method that directly constructs such diverse committees using arti cial training data. This paper introduces Active-Decorate, which uses Decorate committees to select good training examples.

Classi cation modeling (a.k.a. supervised learning) is an extremely useful analytical technique for developing predictive and forecasting applications. The explosive growth in data warehousing and internet usage has made large amounts of data potentially available for developing classi cation models. For example, natural language text is widely available in many forms (e.g., electronic mail, news articles, reports, and web page contents). Categorization of data is a common activity which can be automated to a large extent using supervised learning methods. Examples of this include routing of electronic mail, satellite image classi cation, and character recognition. However, these tasks require labeled data sets of su ciently high quality with adequate instances for training the predictive models. Much of the on-line data, particularly the unstructured variety (e.g., text), is unlabeled. Labeling is usually a expensive manual process done by domain experts. Active learning is an approach to solving this problem and works by identifying a subset of the data that needs to be labeled and uses this subset to generate classi cation models. We present an active learning method that uses adaptive resampling in a natural way to signi cantly reduce the size of the required labeled set and generates a classi cation model that achieves the high accuracies possible with current adaptive resampling methods.

For many supervised learning tasks it is very costly to produce training data with class labels. Active learning acquires data incrementally, at each stage using the model learned so far to help identify especially useful additional data for labeling. Existing empirical active learning approaches have focused on learning classifiers. However, many applications require estimations of the probability of class membership, or scores that can be used to rank new cases. We present a new active learning method for class probability estimation (CPE) and ranking. BOOTSTRAP-LV selects new data for labeling based on the variance in probability estimates, as determined by learning multiple models from bootstrap samples of the existing labeled data. We show empirically that the method reduces the number of data items that must be labeled, across a wide variety of data sets. We also compare BOOTSTRAP-LV with UNCERTAINTY SAMPLING, an existing active learning method designed to maximize classification accuracy. The results show that BOOTSTRAP-LV dominates for CPE. Surprisingly it also often is preferable for accelerating simple accuracy maximization. 1

Image annotations allow users to access a large image database with textual queries. There have been several studies on automatic image annotation utilizing machine learning techniques, which automatically learn statistical models from annotated images and apply them to generate annotations for unseen images. One common problem shared by most previous learning approaches for automatic image annotation is that each annotated word is predicated for an image independently from other annotated words. In this paper, we proposed a coherent language model for automatic image annotation that takes into account the word-toword correlation by estimating a coherent language model for an image. This new approach has two important advantages: 1) it is able to automatically determine the annotation length to improve the accuracy of retrieval results, and 2) it can be used with active learning to significantly reduce the required number of annotated image examples. Empirical studies with Corel dataset are presented to show the effectiveness of the coherent language model for automatic image annotation. Categories and Subject Descriptors

We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. 1.

The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning has attracted much attention. Previous research on semi-supervised learning mainly focuses on semi-supervised classification. Although regression is almost as important as classification, semi-supervised regression is largely understudied. In particular, although co-training is a main paradigm in semi-supervised learning, few works has been devoted to co-training style semi-supervised regression algorithms. In this paper, a co-training style semi-supervised regression algorithm, i.e. COREG, is proposed. This algorithm uses two regressors each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean square error over the labeled neighborhood of that example. Analysis and experiments show that COREG can effectively exploit unlabeled data to improve regression estimates.