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

Titanium Dioxide Nanomaterials: Synthesis, Properties, Modifications, and Applications

Data Mining - Concepts and Techniques.

https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/pr07.pdf

ML-KNN: A lazy learning approach to multi-label learning

Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

/pdf/multi-label-classification-an-overview-3lubavw9dw.pdf

Multi-label classification: An overview

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Zhang%20-%20Multi-Label%20Learning%20Algorithms%20-%202013.pdf

A Review On Multi-Label Learning Algorithms

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backpropagation for multilabel learning, is proposed. It is derived from the popular backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms

Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

Titanium dioxide (TiO2 ) nanocrystals are prepared by a hydrolysis process of tetrabutyl titanate. Nanocrystal samples with various sizes of 6.8-27.9 nm are obtained after annealing from 100 to 650 °C. The crystal structures and the average particle sizes are examined using x-ray diffraction. Raman scattering was employed to investigate the evolution of the anatase phase in the nanocrystals during annealing. Phonon confinement and non-stoichiometry effects are responsible for the blueshift and broadening of the lowest-frequency Eg Raman mode. The influence of interfacial vibrations on the Raman linewidth is also discussed.

https://iopscience.iop.org/article/10.1088/0022-3727/33/8/305/pdf

Raman scattering study on anatase TiO2 nanocrystals

In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels Such a problem can occur in many real-world tasks, eg an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multi-instance learning and multi-label learning Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification

/pdf/multi-instance-multi-label-learning-with-application-to-3yreihjqan.pdf

Min-Ling Zhang

Papers

ML-KNN: A lazy learning approach to multi-label learning

A Review On Multi-Label Learning Algorithms

Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

Raman scattering study on anatase TiO2 nanocrystals

Multi-Instance Multi-Label Learning with Application to Scene Classification