机器学习应用与趋势手册：算法、方法和技术

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"《机器学习应用与趋势手册：算法、方法和技术》是一部深入探讨机器学习领域的多篇章专著，由多位国际知名学者共同撰写。本书分为多个章节，涵盖了数据聚类分析、主图与流形、径向基函数学习算法、多目标优化的自然启发式方法、异常检测的人工免疫系统、机器学习模型的校准、不完全数据分类、多元时间序列的聚类与可视化、局部循环神经网络及其应用、非平稳信号分析的核机器以及迁移学习等多个关键主题。此外，还讨论了个性化贫血治疗中的机器学习应用和确定性基因序列模式挖掘。该书旨在为研究者、学生和从业人员提供关于机器学习最新进展和应用的全面理解。" 在本手册中，我们可以了解到： 1. 数据聚类分析：由Rui Xu和Donald C. Wunsch II探讨，聚类分析是一种无监督学习方法，用于发现数据的内在结构和未知模式，是理解和探索大规模数据集的重要工具。 2. 主图与流形：Alexander N. Gorban和Andrei Y. Zinovyev介绍了如何利用主图和流形理论来理解复杂数据的结构和特征。 3. 径向基函数学习算法：Lei Xu的研究关注于RBF函数和基于子空间的学习算法，这些算法在函数逼近和分类任务中具有广泛应用。 4. 多目标优化的自然启发式方法：Sanjoy Das等人讨论了如遗传算法和粒子群优化等自然启发式策略，用于解决多目标优化问题。 5. 异常检测的人工免疫系统：Eduard Plett等人展示了如何利用人工免疫系统来识别和处理数据中的异常现象，这对于网络安全和故障检测等领域尤其重要。 6. 机器学习模型的校准：Antonio Bella等人详细阐述了校准机器学习模型的方法，以提高预测的准确性和可靠性。 7. 不完全数据分类：Pedro J. García-Laencina等人探讨了在数据缺失情况下进行有效分类的策略，这对于实际数据集中的不完整数据处理至关重要。 8. 多元时间序列的聚类与可视化：Alfredo Vellido和Iván Olier研究了如何对复杂的时间序列数据进行聚类和可视化，以便于理解和分析动态模式。 9. 局部循环神经网络及其应用：Todor D. Ganchev介绍了局部循环神经网络的原理和在序列数据处理中的应用，如语音识别和自然语言处理。 10. 非平稳信号分析的核机器：Paul Honeine等人利用核方法来分析非平稳信号，揭示信号随时间变化的特性。 11. 迁移学习：Lisa Torrey和Jude Shavlik讨论了如何将已学习的知识转移到新的任务或环境中，这对于有限标记数据的学习任务尤其有用。 12. 个性化贫血治疗中的机器学习：Adam E. Gaweda研究了机器学习在定制贫血治疗方案中的应用，展示其在医疗决策支持系统中的潜力。 13. 确定性基因序列模式挖掘：Pedro Gabriel Ferreira探讨了如何通过确定性的模式挖掘方法发现基因序列中的规律，为生物信息学研究提供了新视角。《机器学习应用与趋势手册》是理解机器学习最新进展、算法创新和实际应用的宝贵资源，对于研究人员、工程师和学生来说，是一本不可或缺的参考书籍。

Chapter 8

Clustering and Visualization of Multivariate Time Series .................................................................. 176

Alfredo Vellido, Universidad Politécnica de Cataluña, Spain

Iván Olier, Universidad Politécnica de Cataluña, Spain

The exploratory investigation of multivariate time series (MTS) may become extremely difcult, if not

impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on

the exploration of MTS through unsupervised clustering and visualization. In this chapter, the authors

describe generative topographic mapping through time (GTM-TT), a model with foundations in prob-

ability theory that performs such tasks. The standard version of this model has several limitations that

limit its applicability. Here, the authors reformulate it within a Bayesian approach using variational

techniques. The resulting variational Bayesian GTM-TT, described in some detail, is shown to behave

very robustly in the presence of noise in the MTS, helping to avert the problem of data overtting.

Chapter 9

Locally Recurrent Neural Networks and Their Applications.............................................................. 195

Todor D. Ganchev, University of Patras, Greece

In this chapter the author reviews various computational models of locally recurrent neurons and de-

liberates the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based

on them. Generalizations of these structures are discussed as well. Furthermore, the author points at a

number of real-world applications of LRNNs that have been reported in past and recent publications.

These applications involve classication or prediction of temporal sequences, discovering and modeling of

spatial and temporal correlations, process identication and control, etc. Validation experiments reported

in these developments provide evidence that locally recurrent architectures are capable of identifying

and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the

main reason for their advantageous performance when compared with the one of their non-recurrent

counterparts or other reasonable machine learning techniques.

Chapter 10

Nonstationary Signal Analysis with Kernel Machines ....................................................................... 223

Paul Honeine, Institut Charles Delaunay, France

Cédric Richard, Institut Charles Delaunay, France

Patrick Flandrin, Ecole Normale Supérieure de Lyon, France

This chapter introduces machine learning for nonstationary signal analysis and classication. It argues

that machine learning based on the theory of reproducing kernels can be extended to nonstationary

signal analysis and classication. The authors show that some specic reproducing kernels allow pat-

tern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the

selection of the reproducing kernel for a nonstationary signal classication problem. For this purpose,

the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency

representation for a given classication problem. These links offer new perspectives in the eld of

nonstationary signal analysis, which can benet from recent developments of statistical learning theory

and pattern recognition.

Chapter 11

Transfer Learning ................................................................................................................................ 242

Lisa Torrey, University of Wisconsin, USA

Jude Shavlik, University of Wisconsin, USA

Transfer learning is the improvement of learning in a new task through the transfer of knowledge from

a related task that has already been learned. While most machine learning algorithms are designed to

address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing

interest in the machine-learning community. This chapter provides an introduction to the goals, settings,

and challenges of transfer learning. It surveys current research in this area, giving an overview of the

state of the art and outlining the open problems. The survey covers transfer in both inductive learning

and reinforcement learning, and discusses the issues of negative transfer and task mapping.

Chapter 12

Machine Learning in Personalized Anemia Treatment ....................................................................... 265

Adam E. Gaweda, University of Louisville, USA

This chapter presents application of reinforcement learning to drug dosing personalization in treatment

of chronic conditions. Reinforcement learning is a machine learning paradigm that mimics the trial-

and-error skill acquisition typical for humans and animals. In treatment of chronic illnesses, nding the

optimal dose amount for an individual is also a process that is usually based on trial-and-error. In this

chapter, the author focuses on the challenge of personalized anemia treatment with recombinant human

erythropoietin. The author demonstrates the application of a standard reinforcement learning method,

called Q-learning, to guide the physician in selecting the optimal erythropoietin dose. The author further

addresses the issue of random exploration in Q-learning from the drug dosing perspective and proposes a

“smart” exploration method. Finally, the author performs computer simulations to compare the outcomes

from reinforcement learning-based anemia treatment to those achieved by a standard dosing protocol

used at a dialysis unit.

Chapter 13

Deterministic Pattern Mining On Genetic Sequences ........................................................................ 277

Pedro Gabriel Ferreira, Centre for Genomic Regulation, Spain

Paulo Jorge Azevedo, Universidade do Minho, Portugal

The recent increase in the number of complete genetic sequences freely available through specialized

Internet databases presents big challenges for the research community. One such challenge is the efcient

and effective search of sequence patterns, also known as motifs, among a set of related genetic sequences.

Such patterns describe regions that may provide important insights about the structural and functional

role of DNA and proteins. Two main classes can be considered: probabilistic patterns represent a model

that simulates the sequences or part of the sequences under consideration and deterministic patterns that

either match or not the input sequences. In this chapter a general overview of deterministic sequence

mining over sets of genetic sequences is proposed. The authors formulate an architecture that divides

the mining process workow into a set of blocks. Each of these blocks is discussed individually.

Volume II

Chapter 14

Machine Learning in Natural Language Processing ........................................................................... 302

Marina Sokolova, CHEO Research Institute, Canada

Stan Szpakowicz, University of Ottawa, Canada and Polish Academy of Sciences, Poland

This chapter presents applications of machine learning techniques to traditional problems in natural

language processing, including part-of-speech tagging, entity recognition and word-sense disambigu-

ation. People usually solve such problems without difculty or at least do a very good job. Linguistics

may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the

easy availability of large collections of texts that has made machine learning a method of choice for

processing volumes of data well above the human capacity. One of the main purposes of text processing

is all manner of information extraction and knowledge extraction from such large text. Machine learning

methods discussed in this chapter have stimulated wide-ranging research in natural language processing

and helped build applications with serious deployment potential.

Chapter 15

Machine Learning Applications in Mega-Text Processing ................................................................. 325

Marina Sokolova, CHEO Research Institute, Canada

Stan Szpakowicz, University of Ottawa, Canada and Polish Academy of Sciences, Poland

This chapter presents applications of machine learning techniques to problems in natural language process-

ing that require work with very large amounts of text. Such problems came into focus after the Internet

and other computer-based environments acquired the status of the prime medium for text delivery and

exchange. In all cases which the authors discuss, an algorithm has ensured a meaningful result, be it the

knowledge of consumer opinions, the protection of personal information or the selection of news reports.

The chapter covers elements of opinion mining, news monitoring and privacy protection, and, in parallel,

discusses text representation, feature selection, and word category and text classication problems. The

applications presented here combine scientic interest and signicant economic potential.

Chapter 16

FOL Learning for Knowledge Discovery in Documents .................................................................... 348

Stefano Ferilli, Università degli Studi di Bari, Italy

Floriana Esposito, Università degli Studi di Bari, Italy

Marenglen Biba, Università degli Studi di Bari, Italy

Teresa M. A. Basile, Università degli Studi di Bari, Italy

Nicola Di Mauro,,Università degli Studi di Bari, Italy

This chapter proposes the application of machine learning techniques, based on rst-order logic as a

representation language, to the real-world application domain of document processing. First, the tasks and

problems involved in document processing are presented, along with the prototypical system DOMINUS

and its architecture, whose components are aimed at facing these issues. Then, a closer look is provided

for the learning component of the system, and the two sub-systems that are in charge of performing su-

pervised and unsupervised learning as a support to the system performance. Finally, some experiments

are reported that assess the quality of the learning performance. This is intended to prove to researchers

and practitioners of the eld that rst-order logic learning can be a viable solution to tackle the domain

complexity, and to solve problems such as incremental evolution of the document repository.

Chapter 17

Machine Learning and Financial Investing ......................................................................................... 375

Jie Du, UMBC, USA

Roy Rada, UMBC, USA

This chapter presents the case for knowledge-based machine learning in nancial investing. Machine

learning here, while it will exploit knowledge, will also rely heavily on the evolutionary computation

paradigm of learning, namely reproduction with change and selection of the t. The chapter will begin

with a model for nancial investing and then review what has been reported in the literature as regards

knowledge-based and machine-learning-based methods for nancial investing. Finally, a design of a

nancial investing system is described which incorporates the key features identied through the litera-

ture review. The emerging trend of incorporating knowledge-based methods into evolutionary methods

for nancial investing suggests opportunities for future researchers.

Chapter 18

Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products ....... 387

Yong Yu, The Hong Kong Polytechnic University, Hong Kong

Tsan-Ming Choi, The Hong Kong Polytechnic University, Hong Kong

Kin-Fan Au, The Hong Kong Polytechnic University, Hong Kong

Zhan-Li Sun, The Hong Kong Polytechnic University, Hong Kong

The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation

and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN approach

with a direct binary representation to every single neural network connection is proposed in this chapter

for sales forecasting of fashionable products. In this chapter, the authors will rst explore the details on

how an evolutionary computation approach can be applied in searching for a desirable network struc-

ture for establishing the appropriate sales forecasting system. The optimized ENN structure for sales

forecasting is then developed. With the use of real sales data, the authors compare the performances of

the proposed ENN forecasting scheme with several traditional methods which include articial neural

network (ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outper-

forms other methods. Insights regarding the applications of ENN for forecasting sales of fashionable

products are generated. Future research directions are outlined.

Chapter 19

Support Vector Machine based Hybrid Classiers and Rule Extraction thereof:

Application to Bankruptcy Prediction in Banks ................................................................................. 404

M. A. H. Farquad, Institute for Development and Research in Banking Technology (IDRBT),

India and University of Hyderabad, India

V. Ravi, Institute for Development and Research in Banking Technology (IDRBT), India

Raju S. Bapi, University of Hyderabad, India

Support vector machines (SVMs) have proved to be a good alternative compared to other machine

learning techniques specically for classication problems. However just like articial neural networks

(ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in

the process of training, which is very crucial in some applications like medical diagnosis, security and

bankruptcy prediction etc. In this chapter a novel hybrid approach for fuzzy rule extraction based on

SVM is proposed. This approach handles rule-extraction as a learning task, which proceeds in two major

steps. In the rst step the authors use labeled training patterns to build an SVM model, which in turn

yields the support vectors. In the second step extracted support vectors are used as input patterns to fuzzy

rule based systems (FRBS) to generate fuzzy “if-then” rules. To study the effectiveness and validity of

the extracted fuzzy rules, the hybrid SVM+FRBS is compared with other classication techniques like

decision tree (DT), radial basis function network (RBF) and adaptive network based fuzzy inference

system. To illustrate the effectiveness of the hybrid developed, the authors applied it to solve a bank

bankruptcy prediction problem. The dataset used pertain to Spanish, Turkish and US banks. The quality

of the extracted fuzzy rules is evaluated in terms of delity, coverage and comprehensibility.

Chapter 20

Data Mining Experiences in Steel Industry ........................................................................................ 427

Joaquín Ordieres-Meré, Universidad Politécnica de Madrid, Spain

Ana González-Marcos, Universidad de La Rioja, Spain

Manuel Castejón-Limas, Universidad de León, Spain

Francisco J. Martínez-de-Pisón, Universidad de La Rioja, Spain

This chapter reports ve experiences in successfully applying different data mining techniques in a hot-

dip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models

either for their processes or products using classical techniques. Their need to continuously cut costs

down while increasing productivity and product quality is now pushing the industry into using data

mining techniques so as to gain deeper insights into their manufacturing processes. The authors’ work

was aimed at extracting hidden knowledge from massive data bases in order to improve the existing

control systems. The results obtained, though small at rst glance, lead to huge savings at such high

volume production environment. The effective solutions provided by the use of data mining techniques

along these projects encourages the authors to continue applying this data driven approach to frequent

hard-to-solve problems in the steel industry.

Chapter 21

Application of Neural Networks in Animal Science ........................................................................... 440

Emilio Soria, Valencia University, Spain

Carlos Fernández, Valencia Polytechnic University, Spain

Antonio J. Serrano, Valencia University, Spain

Marcelino Martínez, Valencia University, Spain

José R. Magdalena, Valencia University, Spain

Juan F. Guerrero, Valencia University, Spain

Stock breeding has been one of the most important sources of food and labour throughout human his-

tory. Every advance in this eld has always led to important and benecial impacts on human society.

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