Segment-level probabilistic sequence kernel and segment-level pyramid match kernel based extreme learning machine for classification of varying length patterns of speech

Dynamic kernel (DK)-based support vector machines are used for the classification of varying length patterns. This paper explores the use of intermediate matching kernel (IMK) as a DK for classification of varying length patterns of long duration speech represented as sets of feature vectors. The main issue in construction of IMK is the choice for the set of virtual feature vectors used to select the local feature vectors for matching. This paper proposes to use components of class-independent Gaussian mixture model (CIGMM) as a representation for the set of virtual feature vectors. For every component of CIGMM, a local feature vector each from the two sets of local feature vectors that has the highest probability of belonging to that component is selected and a base kernel is computed between the selected local feature vectors. The IMK is computed as the sum of all the base kernels corresponding to different components of CIGMM. It is proposed to use the responsibility term weighted base kernels in computation of IMK to improve its discrimination ability. This paper also proposes the posterior probability weighted DKs (including the proposed IMKs) to improve their classification performance and reduce the number of support vectors. The performance of the support vector machine (SVM)-based classifiers using the proposed IMKs is studied for speech emotion recognition and speaker identification tasks and compared with that of the SVM-based classifiers using the state-of-the-art DKs.

Credit risk evaluation has become an increasingly important field in financial risk management for financial institutions, especially for banks and credit card companies. Many data mining and statistical methods have been applied to this field. Extreme learning machine (ELM) classifier as a type of generalized single hidden layer feed-forward networks has been used in many applications and achieve good classification accuracy. Thus, we use ELM (kernel based) as a classification tool to perform the credit risk evaluation in this paper. The simulations are done on two credit risk evaluation datasets with three different kernel functions. Simulation results show that the kernel based ELM is more suitable for credit risk evaluation than the popular used Support Vector Machines (SVMs) with consideration of overall, good and bad accuracies.

Manifold learning in local tangent space via extreme learning machine

Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.

Extreme learning machine (ELM) was extended from the generalized single hidden layer feedforward networks where the input weights of the hidden layer nodes can be assigned randomly. It has been widely used for its much faster learning speed and less manual works. Considering the field of multi-label text classification, in this paper, we propose an ELM based algorithm combined with \(L_{21}\)-norm minimization of the output weights matrix called \(L_{21}\)-norm Minimization ELM, which not only fully inherits the merits of ELM but also facilitates group sparsity and reduces complexity of the learning model. Extensive experiments on several benchmark data sets show a more desirable performance compared with other common multi-label classification algorithms.

Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of , justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.

Deep and wide feature based extreme learning machine for image classification

With multiple antennas’ transmission, multiple-input multiple-output (MIMO) technique is able to utilize the space diversity to obtain high spectrum efficiency. In this study, we propose a single hidden layer feedforward network trained with extreme learning machine (ELM) to estimate channel performances in MIMO system. Bit error rate (BER) and signal-to-noise ratio (SNR) performance of back-propagation (BP) algorithm are also compared with our proposed neural network. The simulation results show that ELM has got much better time efficiency than BP in MIMO modeling. Furthermore, MIMO modeling based on ELM doesn’t need to send pilot, which reduce the waste of spectrum resources.

Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.

In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction.

Recently, there has been an ever-increasing interest in the study of data-driven analytics to predict hidden dangers in the cloud service-based coal mine production, with the purpose of the prevention of possible accidents. In this paper, to achieve the above prediction, a machine learning algorithm based on the single-hidden layer feed forward network (SLFN) using timeliness managing extreme learning machine (TMELM) is utilized. Compared with those traditional learning algorithms, extreme learning machine (ELM) has its unique feature of a higher generalization capability at a much faster learning speed. In addition, the timeliness managing ELM has been proposed by incorporating timeliness management scheme into ELM approach. Under the timeliness managing ELM scheme used to predict the hidden dangers, the newly incremental data could be prior to the historical data while maximizing the contribution of the newly increasing training data, since it may be more feasible that the incremental data can contribute reasonable weights to represent the current production situation in accordance with the practical analysis for accidents in coal mine production. The experimental results on the coal mines of Beijing show that by using timeliness managing ELM, the prediction accuracy of hidden danger can be improved with better stability compared with other similar machine learning methods.

With growth of data sets, the efficiency of Extreme Learning Machine (ELM) model combined with accustomed hardware implementation such as Field-programmable gate array (FPGA) became attractive for many real-time learning tasks. In order to reduce resource occupation in eventual trained model on FPGA, it is more efficient to store fixed-point data rather than double-floating data in the on-chip RAMs. This paper conducts the fixed-point evaluation of ELM for classification. We converted the ELM algorithm into a fixed-point version by changing the operation type, approximating the complex function and blocking the large-scale matrixes, according to the architecture ELM would be implemented on FPGA. The performance of classification with single bit-width and mixed bit-width were evaluated respectively. Experimental results show that the fixed-point representation used on ELM does work for some application, while the performance could be better if we adopt mixed bit-width.

It is well-known that solar power depends on many complex parameters such as humidity, radiation, temperature, dust and wind speed. In order to cope with these complex structures and to provide an accurate forecast, the development of reliable and effective forecasting methodology is very significant. In this study, an Artificial Neural Network (ANN)-based system has been developed to forecast very short-term (2 to 4-h) power output of a grid tied Photovoltaic Power Plant (PVPP). An algorithm called Extreme Learning Machine (ELM) has gained increasing interest through its extremely fast learning and good generalization capability. The Kernel Extreme Learning Machine (KELM) which is the improved version of the ELM is proposed to develop a very short-term PVPP power forecast system. The most important feature of KELM is that it has less adjustable parameters and better generalization ability when compared with classical ELM. Experimental studies have been carried out on a grid tied PVPP that has 1 (MW) installed power capacity. The inputs of the KELM-based forecast system are selected as solar power, humidity, radiation and temperature. All data are divided into four parts to analyze the effect of seasons on the performance of the proposed forecast system. The comparison studies are carried out to clearly observe the forecast ability and performance of the KELM. From the experimental studies of the winter season which has not symmetric diurnal mean power curve, Correlation Coefficients (R) values of KELM are calculated as 0.896 for 2-h ahead, 0.865 for 3-h ahead, 0.833 for 4-h ahead. Those of ELM and (Levenberg Marquardt) LM are 0.866–0.812 for 2-h ahead, 0.831–0.669 for 3-h ahead, 0.799–0.502 for 4-h ahead, respectively. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of KELM for winter season are 12.08%-6.14% for 2-h ahead, 14.06%-7.43% for 3-h ahead, 15.33%-7.74% for 4-h ahead, respectively. While these values of ELM are obtained as 13.82%-7.05% for 2-h ahead, 15.52%-8.77% for 3-h ahead, 17.63%-9.33% for 4-h ahead. The RMSE and MAE values of LM are 17.90%-9.14% for 2-h ahead, 20.04%-9.85% for 3-h ahead, 21.22%-10.68% for 4-h ahead. According to the obtained results, it is clearly seen that KELM provides a more powerful and reliable forecasting performance.

Two-stage extreme learning machine for regression

Image receptive fields for artificial neural networks