Abstract Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass can be categorized as (1) decomposition into binary (2) extension from (3) hierarchical classification. Decomposing set classifications that efficiently solved by using classifiers, called class binarization, which popular technique for Neuroevolution, general powe...

We study backpropagation networks learning classiication problems with multiple classes k > 3. The common way to code the output of a network is the one-per-class (OPC) method, where one bit is assigned to each class. A technique called error-correcting output coding (ECOC) converts the k-class learning problem into a large number of two-class learning problems. We propose to use modular archit...

This paper describes a new technique for solving multiclass learning problems by combining Freund and Schapire’s boosting algorithm with the main ideas of Dietterich and Bakiri’s method of error-correcting output codes (ECOC). Boosting is a general method of improving the accuracy of a given base or “weak” learning algorithm. ECOC is a robust method of solving multiclass learning problems by re...

Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around globe. In article, we conducted a study on students’ learning behavior analysis student performance prediction based data about logs three consecutive years college’s “Java Language Programming” course. Firstly, from diverse platforms such as MOOC, Rain Classroom, PTA...

A polychotomizer which assigns the input to one ofK K is constructed using a set of dichotomizers which assign the input to one of two classes We propose techniques to construct a set of linear dichotomizers whose combined decision forms a nonlinear polychotomizer to extract structure from data One way is using error correcting output codes ECOC We propose to incorporate soft weight sharing in ...

Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multiclass learning techniques. In this sens...

0167-8655/$ see front matter 2011 Published by doi:10.1016/j.patrec.2011.09.023 ⇑ Corresponding author at: Centre de Visió per Com O, 08193 Bellaterra, Barcelona, Spain. E-mail addresses: [email protected] (M.Á. B (S. Escalera), [email protected] (X. Baró), petia@maia maia.uab.es (J. Vitriá), [email protected] (O. Pujol). The classification of large number of object categories is a challenging t...

Within the context face expression classi cation using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). The method adopted is to train a single error-correcting output code (ECOC) multiclass classi er to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to ca...

Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of cod...

A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of th...