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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

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

It is well known that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons (including biases) can learn N distinct samples (x/sub i/,t/sub i/) with zero error, and the weights connecting the input neurons and the hidden neurons can be chosen "almost" arbitrarily. However, these results have been obtained for the case when the activation function for the hidden neurons is the signum function. This paper rigorously proves that standard single-hidden layer feedforward networks (SLFNs) with at most N hidden neurons and with any bounded nonlinear activation function which has a limit at one infinity can learn N distinct samples (x/sub i/,t/sub i/) with zero error. The previous method of arbitrarily choosing weights is not feasible for any SLFN. The proof of our result is constructive and thus gives a method to directly find the weights of the standard SLFNs with any such bounded nonlinear activation function as opposed to iterative training algorithms in the literature.

Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.

Hierarchical Clustering for Software Architecture Recovery

Multilayer perceptrons with hard-limiting (signum) activation functions can form complex decision regions. It is well known that a three-layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two-layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN) with any continuous bounded nonconstant activation function or any arbitrary bounded (continuous or not continuous) activation function which has unequal limits at infinities (not just perceptrons) can form disjoint decision regions with arbitrary shapes in multidimensional cases, SLFN with some unbounded activation function can also form disjoint decision regions with arbitrary shapes.

Classification ability of single hidden layer feedforward neural networks

Software systems need to evolve as business requirements, technology and environment change. As software is modified to accommodate the required changes, its structure deteriorates. There is increased deviation from the actual design and architecture. Very often, documentation is not updated to reflect these changes thus making it more and more difficult to understand, manage and maintain these systems. Researchers have applied various techniques to recover the components and architecture of such software systems. The use of clustering techniques has recently been explored for reverse engineering and software architecture recovery. There is a need to tailor clustering algorithms and similarity measures to cater to software. We present a new algorithm for finding intercluster distance. We compare the performance of some popular similarity measures for this algorithm using two test systems and suggest variations of the similarity measures which show better results for software clustering.

The weighted combined algorithm: a linkage algorithm for software clustering

We analyzed Balanced Accuracy (ACC2)feature ranking metrics and identified its draw backs.We proposed to normalize Balanced Accuracy by minimum of tpr and fpr values.We compared results of proposed feature ranking metric with seven well known feature ranking metrics on seven datasets.Newly proposed metric outperforms in more than 60% cases of our experimental trials. The goal of feature selection in text classification is to choose highly distinguishing features for improving the performance of a classifier. The well-known text classification feature selection metric named balanced accuracy measure (ACC2) (Forman, 2003) evaluates a term by taking the difference of its document frequency in the positive class (also known as true positives) and its document frequency in the negative class (also known as false positives). This however results in assigning equal ranks to terms having equal difference, ignoring their relative document frequencies in the classes. In this paper we propose a new feature ranking (FR) metric, called normalized difference measure (NDM), which takes into account the relative document frequencies. The performance of NDM is investigated against seven well known feature ranking metrics including odds ratio (OR), chi squared (CHI), information gain (IG), distinguishing feature selector (DFS), gini index (GINI) ,balanced accuracy measure (ACC2) and Poisson ratio (POIS) on seven datasets namely WebACE(WAP,K1a,K1b), Reuters (RE0, RE1),spam email dataset and 20 newsgroups using the multinomial naive Bayes (MNB) and supports vector machines (SVM) classifiers. Our results show that the NDM metric outperforms the seven metrics in 66% cases in terms of macro-F1 measure and in 51% cases in terms of micro F1 measure in our experimental trials on these datasets.

Haroon A. Babri

Papers

Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

Hierarchical Clustering for Software Architecture Recovery

Classification ability of single hidden layer feedforward neural networks

The weighted combined algorithm: a linkage algorithm for software clustering

Feature selection based on a normalized difference measure for text classification