Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.

Extreme Learning Machine for Regression and Multiclass Classification

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.

Extreme learning machines: a survey

Weighted extreme learning machine for imbalance learning

Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems ELM is based on empirical risk minimization theory and its learning process needs only a single iteration The algorithm avoids multiple iterations and local minimization It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future

Extreme learning machine: algorithm, theory and applications

Extreme Learning Machine proposed by Huang G-B has attracted many attentions for its extremely fast training speed and good generalization performance. But it still can be considered as empirical risk minimization theme and tends to generate over-fitting model. Additionally, since ELM doesn't considering heteroskedasticity in real applications, its performance will be affected seriously when outliers exist in the dataset. In order to address these drawbacks, we propose a novel algorithm called Regularized Extreme Learning Machine based on structural risk minimization principle and weighted least square. The generalization performance of the proposed algorithm was improved significantly in most cases without increasing training time.

Regularized Extreme Learning Machine

Ordinal extreme learning machine

Because of long-consuming training or similarity computing, most traditional collaborative filtering algorithms are off-line methods and can’t be applied in collaborative-filtering services that have accumulated large amounts of data and need to compute predictions under real-time conditions. In order to address this problem, we propose a novel real-time collaborative filtering algorithm, called RCF, based on Extreme Learning Machine (ELM). The initial training and updating of RCF are very fast and can be finished in real time. The experimental results show that the mean recommendation time of RCF is shorter than SVD/ANN and correlation-based algorithms reported in other papers while the accuracy is better.

Real-Time Collaborative Filtering Using Extreme Learning Machine

Adaptive personalized recommendation based on adaptive learning

The presence of fewer samples and large number of input features increases the complexity of the classifier and degrades the stability. Thus, dimension reduction was always carried before supervised learning algorithms such as neural network. This two-stage framework is somewhat redundant in dimension reduction and network training. This paper proposes a novel one-stage learning algorithm for high-dimension small-sample data, called Projection Vector Machine (PVM), which combines dimension reduction with network training and removes the redundancy. Through dimension reduction operation such as singular vector decomposition (SVD), we not only reduce the dimension but also obtain the size of single-hidden layer feedforward neural network (SLFN) and input weight values simultaneously. This size-fixed network will become linear programming system and thus the output weights can be determined by simple least square method. Unlike traditional backpropagation feedforward neural network (BP), parameters in PVM don't need iterative tuning and thus its training speed is much faster than BP. Unlike extreme learning machine (ELM) proposed by Huang [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (2006) 489–501] which assigns input weights randomly, PVM's input weights are ranked by singular values and select the optimal weights order by singular value. We give proof that PVM is a universal approximator for high-dimension small-sample data. Experimental results show that the proposed one-stage algorithm PVM is faster than two-stage learning approach such as SVD+BP and SVD+ELM.

Projection Vector Machine: One-stage learning algorithm from high-dimension small-sample data

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