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

An Introduction to MultiAgent Systems.

Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Frenay%20-%20Classification%20in%20the%20Presence%20of%20Label%20Noise%20-%202014.pdf

Classification in the Presence of Label Noise: A Survey

Power Aware Routing in Mobile Ad Hoc Networks

Characterizing and profiling scientific workflows

Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds

Today, the high cost of supercomputers in the one hand and the need for large-scale computational resources on the other hand, has led to use network of computational resources known as Grid. Numerous research groups in universities, research labs, and industries around the world are now working on a type of Grid called Computational Grids that enable aggregation of distributed resources for solving large-scale data intensive problems in science, engineering, and commerce. Several institutions and universities have started research and teaching programs on Grid computing as part of their parallel and distributed computing curriculum. To better use tremendous capabilities of this distributed system, effective and efficient scheduling algorithms are needed. In this paper, we introduce a new scheduling algorithm based on two conventional scheduling algorithms, Min-Min and Max-Min, to use their cons and at the same time, cover their pros. It selects between the two algorithms based on standard deviation of the expected completion time of tasks on resources. We evaluate our scheduling heuristic, the Selective algorithm, within a grid simulator called GridSim. We also compared our approach to its two basic heuristics. The experimental results show that the new heuristic can lead to significant performance gain for a variety of scenarios.

A Min-Min Max-Min selective algorihtm for grid task scheduling

Recently, utility Grids have emerged as a new model of service provisioning in heterogeneous distributed systems. In this model, users negotiate with service providers on their required Quality of Service and on the corresponding price to reach a Service Level Agreement. One of the most challenging problems in utility Grids is workflow scheduling, i.e., the problem of satisfying the QoS of the users as well as minimizing the cost of workflow execution. In this paper, we propose a new QoS-based workflow scheduling algorithm based on a novel concept called Partial Critical Paths (PCP), that tries to minimize the cost of workflow execution while meeting a user-defined deadline. The PCP algorithm has two phases: in the deadline distribution phase it recursively assigns subdeadlines to the tasks on the partial critical paths ending at previously assigned tasks, and in the planning phase it assigns the cheapest service to each task while meeting its subdeadline. The simulation results show that the performance of the PCP algorithm is very promising.

/pdf/cost-driven-scheduling-of-grid-workflows-using-partial-3xzo3huk5k.pdf

Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths

Recently, utility grids have emerged as a new model of service provisioning in heterogeneous distributed systems. In this model, users negotiate with providers on their required Quality of Service and on the corresponding price to reach a Service Level Agreement. One of the most challenging problems in utility grids is workflow scheduling, i.e., the problem of satisfying users' QoS as well as minimizing the cost of workflow execution. In this paper, we propose a new QoS-based workflow scheduling algorithm based on a novel concept called Partial Critical Path. This algorithm recursively schedules the critical path ending at a recently scheduled node. The proposed algorithm tries to minimize the cost of workflow execution while meeting a user-defined deadline. The simulation results show that the performance of our algorithm is very promising.

/pdf/cost-driven-scheduling-of-grid-workflows-using-partial-4l5hhujv5a.pdf

Cost-driven scheduling of grid workflows using Partial Critical Paths

This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both Support Vector Machine (SVM) and Genetic Algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm is used to improve the generalization performance of the SVM classifier. In order to do this, the design of the SVM classifier is optimized by searching for the best value of the parameters that tune its discriminate function, and looking for the best subset of features that optimizes the classification fitness function. Experimental results demonstrate that the approach adopted better classifies ECG signals. Four types of arrhythmias were distinguished with 93% accuracy.

/pdf/ecg-arrhythmia-classification-with-support-vector-machines-3xzqmnr3yq.pdf

Mahmoud Naghibzadeh

Papers

Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds

A Min-Min Max-Min selective algorihtm for grid task scheduling

Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths

Cost-driven scheduling of grid workflows using Partial Critical Paths

ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm