机器学习应用与趋势探索：算法与技术解析

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"机器学习应用与趋势研究手册算法,方法与技术"是一本由西班牙瓦伦西亚大学的学者们编撰的专业书籍，涵盖了机器学习领域的广泛主题，包括算法、方法和技术。该书由Emilio Soria Olivas、José David Martín Guerrero、Marcelino Martínez Sober、Jose Rafael Magdalena Benedito和Antonio José Serrano López等专家共同撰写。在机器学习这一快速发展的领域中，这本书提供了深入的洞察和最新的研究成果。它详细探讨了各种机器学习算法，这些算法是人工智能和数据科学的核心。其中包括监督学习（如支持向量机、决策树和神经网络）、无监督学习（如聚类和降维）以及半监督和强化学习等不同类别。每种算法的原理、实现细节和适用场景都得到了详尽的阐述，帮助读者理解它们的工作机制和优缺点。此外，书中还讨论了机器学习的方法论，如特征选择、模型评估和优化、超参数调优等关键步骤，这些都是构建高效机器学习模型的关键。作者们还介绍了集成学习（如随机森林和梯度提升）以及深度学习，特别是卷积神经网络和递归神经网络在图像识别和自然语言处理中的应用。随着大数据和云计算的发展，机器学习技术在各个行业的应用日益广泛。本书也涵盖了这些趋势，分析了机器学习如何在医疗健康、金融、社交媒体、物联网(IoT)等领域创造价值。同时，书中也讨论了隐私保护、数据安全和伦理问题，这些都是随着机器学习广泛应用而产生的挑战。最后，本书还探讨了未来的研究方向，如迁移学习、元学习、自动机器学习( AutoML)以及量子计算对机器学习的影响。通过这些前沿话题，读者可以了解到机器学习领域的最新进展和潜在机遇。《机器学习应用与趋势研究手册：算法、方法与技术》是理解和掌握机器学习理论与实践的重要资源，适合研究人员、学生以及在相关领域工作的专业人士阅读。它不仅提供了丰富的理论知识，还结合实际案例展示了机器学习在现实生活中的应用，有助于读者提升技能并跟上这个快速发展领域的步伐。

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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