This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, mine more effective algorithm. Firstly, the reflectance spectra of leaves in were analyzed. Then, solve problem that 0-element sparse random (SR) coding matrices affects classification performance error-correcting output codes (ECOC), two versions super...

A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). One of the main requirements of the ECOC design is that the base classifier is capable of splitting each sub-group of classes from each binary problem. In this paper, we present a novel strategy to model multi-class classification problems using sub-class information in the ECOC framew...

Recent researches have shown that ensembles of classifiers have more accuracy than a single classifier. Baging, boosting and error correcting output codes (ECOC) are most common ways for creating combination of classifiers. In this paper a new method for ensemble of classifiers has been introduced and performance of this method examined by applying to handwritten pen digits dataset. Experimenta...

AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...

This paper applies error-correcting output coding (ECOC) to the task of document cate-gorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classiication problem into many binary classiication tasks, and then combining the results of the subtasks into a hypothesized solution to the original problem. There has been much recent interest in the machine le...

The coding matrix design plays a fundamental role in the prediction performance of the error correcting output codes (ECOC)-based multi-class task. In many-class classification problems, e.g., fine-grained categorization, it is difficult to distinguish subtle between-class differences under existing coding schemes due to a limited choices of coding values. In this paper, we investigate whether ...

Linear Discriminant Analysis (LDA) is one of the learning algorithms for the binary problems. One of the drawbacks of LDA is the degradation of its performance when it is difficult to approximate the boundary between two classes with a linear function, i.e. the boundary is non-linear. In this research, we apply Error-Correcting Output Codes (ECOC) to LDA. ECOC is well-known as one of the ensemb...

This paper applies error-correcting output coding (ECOC) to the task of document cate-gorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classiication problem into many binary classiication tasks, and then combining the results of the subtasks into a hypothesized solution to the original problem. There has been much recent interest in the machine le...

Abstract. We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin-based binary classifiers. An important open problem in this context is how to measure the distance between class codewords and the outputs of the classifiers. In this paper we propose a new decoding function that combines the margins through an estimate of their ...

The multi-class classification is a challenging problem for several applications in Computer Vision. Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding stra...