An experimental comparison of shape classification methods based on autoregressive modeling and Fourier descriptors of closed contours is carried out. The performance is evaluated using two independent sets of data: images of letters and airplanes. Silhouette contours are extracted from non

Kearns introduced the \statistical query" (SQ) model as a general method for producing learning algorithms which are robust against classication noise. We extend this approach in several ways in order to tackle algorithms that use \membership queries", focusing on the more stringent model

In machine learning, class noise occurs frequently and deteriorates the classifier derived from the noisy dataset. This paper presents several possible solutions to this problem based on LSA, a probabilistic noise model proposed by Lawrence and Schölkopf (2001). These solutions include

Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While

Abstract. Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a

The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper shows how hidden Markov models (HMM’s) can be used to build an environmental noise

, car and city street. In this paper we present our hidden Markov model based noise classifier. We describe the architecture of the system, compare classification results from the system with human listening tests, and discuss open issues in environmental noise classification for mobile computing. 1.

are assigned automatically and sometimes contain errors. Since image segmentation plays a crucial role as a preliminary step for high level image processing, the class noise problem needs to be addressed. This paper presents several possible solutions to this problem based on a probabilistic noise model (LSA

ambiguity, leading to the classic “octave error ” in pitch tracking. Moreover, additive noise can affect autocorrelation in ways that are difficult to model. Instead of explicitly using the most obvious features of autocorrelation, we present a trained classifier-based approach which we call Subband

life, such as the office, car and city street. In this paper we present our hidden Markov model based noise classifier. We describe the architecture of our system, the experimental results, and discuss the open issues in environmental noise classification for mobile computing. 1