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

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.

/pdf/a-delay-tolerant-network-architecture-for-challenged-4nomv5ym6e.pdf

A delay-tolerant network architecture for challenged internets

An Introduction to MultiAgent Systems.

The design of route guidance systems faces a well-known dilemma. The approach that theoretically yields the system-optimal traffic pattern may discriminate against some users in favor of others. Proposed alternate models, however, do not directly address the system perspective and may result in inferior performance. We propose a novel model and corresponding algorithms to resolve this dilemma. We present computational results on real-world instances and compare the new approach with the well-established traffic assignment model. The essence of this study is that system-optimal routing of traffic flow with explicit integration of user constraints leads to a better performance than the user equilibrium, while simultaneously guaranteeing superior fairness compared to the pure system optimum.

/pdf/system-optimal-routing-of-traffic-flows-with-user-yfqkabb8tu.pdf

System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion

Ordered sets have recently gained much importance in many applied and theoretical problems in computer science and operations research ranging from project planning via processor scheduling to sorting and retrieval problems. These problems involve partial orders as their basic structure, e.g. as precedence constraints in scheduling problems, or as comparability relation among the objects to be sorted or retrieved.

Computationally Tractable Classes of Ordered Sets

Comparability graphs are undirected graphs that represent the comparability relation of partial orders. They constitute an important interface between graphs and partial orders both for theoretical investigations on their structural properties, and the development of efficient algorithmic methods for otherwise NP-hard combinatorial (optimization) problems on partial orders and their comparability graphs.

Algorithmic Aspects of Comparability Graphs and Interval Graphs

The fastest-known algorithm for recognizing interval graphs [S. Booth and S. Lucker, J. Comput. System Sci., 13 (1976), pp. 335–379] iteratively manipulates the system of all maximal cliques of the given graph in a rather complicated way in order to construct a consecutive arrangement (more precisely, a tree representation of all possible consecutive arrangements). This paper presents a much simpler algorithm using a related, but much more informative tree representation of interval graphs. This tree is constructed in an incremental fashion by adding vertices to the graph in a predefined order such that adding a vertex u takes $O(|{\operatorname{Adj}}(u)| + 1)$ amortized time.

An incremental linear-time algorithm for recognizing interval graphs

We consider the problem of minimizing the resource costs in a project network No subject to a time limit for the completion of No, when resource requirements per activity and costs for obtaining resources are independent of time. Our results show that the optimum is determined for all possible resource requirements and costs by certain sets of "feasible structures," which are networks that extend the precedence relation of No and respect the given time limit. We characterize the least such sets, and give methods for determining them as well as for determining the optimum. Furthermore, we establish duality relations with the problem of scarce resources minimizing project duration subject to limited resources and characterize all "essentially different" problems.

https://www.jstor.org/stable/pdfplus/170523.pdf

Rolf H. Möhring

Papers

System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion

Computationally Tractable Classes of Ordered Sets

Algorithmic Aspects of Comparability Graphs and Interval Graphs

An incremental linear-time algorithm for recognizing interval graphs

Minimizing Costs of Resource Requirements in Project Networks Subject to a Fixed Completion Time