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

https://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf

Extreme learning machine: Theory and applications

Data Mining - Concepts and Techniques.

The Theory of Matrices. By F R. Gantmacher. Two volumes, pp. 374 and 276. 1959. (Translated from the Russian by K. A. Hirsch; Chelsea Publishing Company, New York)

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

Concerns nonlinear model predictive control, and particularly the nonlinear optimization problem. Usually the control sequence can be determined by using some effective numerical iteration approaches, especially for multistep predictive control. This work focuses on the multistep adaptive NMPC controller design using neural-net. The main ideas are (A) initialisation of the multistep control laws by using one-step ahead predictive control law; (B) linearization of the neural-net predictor at every operating point; and (C) tuning of the neural-net predictor through online learning using teacher signals generated by closed-loop system input-output data. As an illustrative example of our approach, an explicit control laws are derived for the control horizon Nu=2 case.

https://ieeexplore.ieee.org/iel3/3920/11355/00532721.pdf

Neural-net based multi-steps nonlinear adaptive model predictive controller design

Graphs traditionally have many applications in various areas of computer science. Research in graph-based data mining has recently gained a high level of attraction due to its broad range of applications. Examples include XML documents, web logs, web searches and molecular biology. Most of the approaches used in these applications focus on deriving interesting, frequent patterns from given datasets. Two fundamental questions are, however, ignored; that is, how to derive a graph from a set of objects and how to order nodes according to their relations with others in the graph. In this paper, we provide approaches to building a graph from a given set of objects accompanied by their feature vectors, as well as to ranking nodes in the graph. The basic idea of our ranking approach is to quantify the important role of a node as the degree to which it has direct and indirect relationships with other nodes in a graph. A method for visualising graphs with ranking nodes is also presented. The visual examples and applications are provided to demonstrate the effectiveness of our approaches.

On graph modelling, node ranking and visualisation

The aim of this paper is to examine the Eigenpaxel and commonly used Gabor wavelet filter in terms of distinguishing capability and robustness with respect to 12 types of variation of a neutral expression frontal face image for retrieval.

Feature Extraction for Face Image Retrieval

This paper addresses the prescribed tracking performance control problem for uncertain steer-by-wire (SbW) systems with input nonlinearity (including dead-zone and actuator fault) and the limitation of controller-area-network (CAN) bandwidth. An adaptive interval type-2 fuzzy logic system (IT2 FLS) is introduced to approximate the lumped model uncertainty, and a switching event-triggering mechanism (ETM) is applied to save the communication resources. Combining the backstepping approach and barrier Lyapunov function techniques, a prescribed tracking performance control method is proposed for SbW systems, where the initial values of state errors are no longer required in the controller design. Theoretical analysis shows that the tracking error can converge to the predefined residual set within preset time instead of the time tending infinite, while the closed-loop system is semi-globally stable. Simulations and vehicle experiments are presented to verify the effectiveness of the proposed control method.

Event-Triggered Prespecified Performance Control for Steer-by-Wire Systems With Input Nonlinearity

While people cluster images in terms of semantics, computers can not do too much on this job due to the use of lower-level features, which is the common way to computer workers to deal with the image recognition. To measure the closeness between feature vectors, similarity metrics play a key role and they directly impact the clustering performance. This paper develops a framework of learning pseudo metrics (LPM) for semantic image clustering practice. Multilayer perceptron (MLP) is employed to model the LPM with a set of criteria on evaluating the LPM quality and availability. Using a standard k-mean clustering technique, a comparative study is carried out to demonstrate the significance of our proposed LPM in semantic image clustering. Experiments show that the LPM-based similarity metric can produce better clustering results in terms of both impurity and robustness.

Dianhui Wang

Papers

Neural-net based multi-steps nonlinear adaptive model predictive controller design

On graph modelling, node ranking and visualisation

Feature Extraction for Face Image Retrieval

Event-Triggered Prespecified Performance Control for Steer-by-Wire Systems With Input Nonlinearity

Learning pseudo metrics for semantic image clustering