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In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems ( i.e. , data sets) with a smaller number of samples ( i.e. , instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data’s distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

In recent years, along with the generation of uncertain data, more and more attention is paid to the mining of uncertain data. In this paper, we study the problem of classifying uncertain data using Extreme Learning Machine (ELM). We first propose the UU-ELM algorithm for classification of uncertain data which is uniformly distributed. Furthermore, the NU-ELM algorithm is proposed for classifying uncertain data which are non-uniformly distributed. By calculating bounds of the probability, the efficiency of the algorithm can be improved. Finally, the performances of our methods are verified through a large number of simulated experiments. The experimental results show that our methods are effective ways to solve the problem of uncertain data classification, reduce the execution time and improve the efficiency. [ABSTRACT FROM AUTHOR]

Driven by the emerging network data exchange and storage, XML documents classification has become increasingly important. Most existing representation model and conventional learning algorithm are defined on certain XML documents. However, in many real-world applications, XML datasets contain inherent uncertainty, which brings greater challenges to classification problem. In this paper, we propose a novel solution to classify uncertain XML documents, including uncertain XML documents representation and two uncertain learning algorithms based on Extreme Learning Machine. Experimental results show that our approaches exhibit prominent performance for uncertain XML documents classification problem. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM), a competitive machine learning technique for single-hidden-layer feedforward neural networks (SLFNNs), has proven to be efficient and effective algorithm for regression and classification problems. However, traditional ELM involves a large number of hidden nodes for complex real world regression and classification problems which increasing the computation burden. In this paper, a decomposition based fast ELM (DFELM) algorithm is proposed to effectively reduce the computational burden for large number of hidden nodes condition. Compared with ELM algorithm, DFELM algorithm has faster training time with a large number of hidden nodes maintaining the same accuracy performance. Experiment on three regression problems, six classification problems and a complex blast furnace modeling problem are carried out to verify the performance of DFELM algorithm. Moreover, the decomposition method can be extended to other modified ELM algorithms to further reduce the training time. [ABSTRACT FROM AUTHOR]

Graph patterns are widely used to define the feature space for building an efficient graph classification model. Synergy graph patterns refer to those graphs, where the relationships among the nodes are highly inseparable. Compared with the general graph patterns, synergy graph patterns which have much higher discriminative powers are more suitable as the classification features. Extreme Learning Machine (ELM) is a simple and efficient Single-hidden Layer Feedforward neural Networks (SLFNs) algorithm with extremely fast learning capacity. In this paper we propose the problem of extending ELM to non-redundant synergy pattern based graph classification. The graph classification framework being widely used consists of two steps, namely feature generation and classification. The first issue is how to quickly obtain significant graph pattern features from a graph database. The next step is how to effectively build a graph classification model with these graph pattern features. An efficient depth-first algorithm, called GINS, was presented to find all non-redundant synergy graph patterns. Also, based on the proposed Support Graph Vector Model (SGVM) and ELM algorithm, the graph classification model was constructed. Extensive experiments are conducted on a series of real-life datasets. The results show that GINS is more efficient than two representative competitors. Besides, when the generated graph patterns are considered as the classification features, the GINS+ELM classification accuracy can be improved much. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are widely used for traffic classification and anomaly detection nowadays, however, how to fast and accurately classify the flows remains extremely challengeable. In this paper, we propose an extreme learning machine (ELM) based algorithm called L1-Norm Minimization ELM, which fully inherits the merits of ELM, and meanwhile, exhibits the sparsity-induced characteristics which could reduce the complexity of learning model. At the evaluation stage, we preprocessed the raw data trace from trans-Pacific backbone link between Japan and the United States, and generated 248 features datasets. The empirical study shows that L1-ELM can achieve good generalization performance on the evaluation datasets, while preserving the fast learning and little human intervened advantages that ELM has. [ABSTRACT FROM AUTHOR]

Extreme Learning Machine (ELM) represents a popular paradigm for training feedforward neural networks due to its fast learning time. This paper applies the technique for the automatic classification of speech utterances. Power Normalized Cepstral Coefficients (PNCC) are employed as feature vectors and ELM performs the final classification. Both the baseline ELM algorithm and ELM with kernel have been employed and tested. Due to the fixed number of input neurons in the ELM, a length normalization algorithm is employed to transform the PNCC sequence into a vector of fixed length. Length normalization has been performed using two techniques: the first is based on Dynamic Time Warping (DTW) distances, the second on the vectorized outerproduct of trajectory matrix. Experiments have been conducted on the TIDIGITS corpus, to assess the performance on an isolated speech recognition task, and on ITAAL, to validate the system in an emergency detection task in realistic acoustic conditions. The ELM approach has been compared to template matching based on Dynamic Time Warping and to a Support Vector Machine based speech recognizer. The obtained results demonstrated the effectiveness of the approach both in terms of recognition performance and execution times. In particular, classification based on PNCCs, DTW distances and ELM kernel resulted in the best performing algorithm both in terms of recognition accuracy and execution times. [ABSTRACT FROM AUTHOR]