Abstract not found

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach istwofold: rst, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classi cation is handled by several support vector machine classi ers arranged in two layers. This architecture is known as Adaptive Combination of Classi ers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is signi cantly better than a full body

Much recent attention, both experimental and theoretical, has been focussed on classification algorithms which produce voted combinations of classifiers. Recent theoretical work has shown that the impressive generalization performance of algorithms like AdaBoost can be attributed to the classifier having large margins on the training data. We present an abstract algorithm for finding linear combinations of functions that minimize arbitrary cost functionals (i.e functionals that do not necessarily depend on the margin). Many existing voting methods can be shown to be special cases of this abstract algorithm. Then, following previous theoretical results bounding the generalization performance of convex combinations of classifiers in terms of general cost functions of the margin, we present a new algorithm (DOOM II) for performing a gradient descent optimization of such cost functions. Experiments on

Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones.

Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that significantly improve classifiers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classifier. In this paper, we present MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classifier. MFS combines multiple NN classifiers each using only a random subset of features. The experimental results are encouraging: On 25 datasets from the UCI Repository, MFS significantly improved upon the NN, k nearest neighbor (kNN), and NN classifiers with forward and backward selection of features. MFS was also robust to corruption by irrelevant features compared to the kNN classifier. Finally, we show that MFS is able to reduce both bias and variance components of error.

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.

Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding, that significantly improve classifiers like decision trees, rule learners, or neural networks. Unfortunately, these combining methods do not improve the nearest neighbor classifier. In this paper, we present MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classifier. MFS combines multiple NN classifiers each using only a random subset of features. The experimental results are encouraging: On 25 datasets from the UCI Repository, MFS signi cantly outperformed several standard NN variants and was competitive with boosted decision trees. In additional experiments, we show that MFS is robust to irrelevant features, and is able to reduce both bias and variance components of error.

Abstract not found

This paper explores two boosting techniques for cost-sensitive tree classi cations in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that the two boosting techniques are a good solution in different aspects under this situation.

Abstract not found