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

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization.

Short Communication: Concept lattice reduction using fuzzy K-Means clustering

The paper surveys methods and approaches for the task of keyword extraction. The systematic review of methods was gathered which resulted in a comprehensive review of existing approaches. Work related to keyword extraction is elaborated for supervised and unsupervised methods, with a special emphasis on graph-based methods. Various graph-based methods are analyzed and compared. The paper provides guidelines for future research plans and encourages the development of new graph-based approaches for keyword extraction.

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An Overview of Graph-Based Keyword Extraction Methods and Approaches

The technique of latent semantic indexing is used in a wide variety of commercial applications. In these applications, the processing time and RAM required for SVD computation, and the processing time and RAM required during LSI retrieval operations are all roughly linear in the number of dimensions, k, chosen for the LSI representation space. In large-scale commercial LSI applications, reducing k values could be of significant value in reducing server costs. This paper explores the effects of varying dimensionality.The approach taken here focuses on term comparisons. Pairs of terms are considered which have strong real-world associations. The proximities of members of these pairs in the LSI space are compared at multiple values of k. The testing is carried out for collections of from one to five million documents. For the five million document collection, a value of k ≈ 400 provides the best performance.The results suggest that there is something of an 'island of stability' in the k = 300 to 500 range. The results also indicate that there is relatively little room to employ k values outside of this range without incurring significant distortions in at least some term-term correlations.

An empirical study of required dimensionality for large-scale latent semantic indexing applications

Modeling the growth pattern of in-season and off-season Ross 308 broiler breeder flocks

Text classification is an important and common task in supervised machine learning. The Naive Bayes Classifier is a popular algorithm that can be used for this purpose. The goal of our research was prediction of song performer using Naive Bayes classification algorithm based solely on lyrics. A dataset that has been created consists of lyrics performed by Nirvana and Metallica, 207 songs in total. Model evaluation measures showed very good results: precision of 0.93, recall of 0.95 and F 1 -measure of 0.94, therefore lyrics classification using Naive Bayes can be considered as successful.

Lyrics classification using Naive Bayes

Comparison of Information Retrieval Techniques: Latent Semantic Indexing and Concept Indexing.

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Comparison of Information Retrieval Techniques: Latent Semantic Indexing and Concept Indexing

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

Using Machine Learning for Web Page Classification in Search Engine Optimization

Our aim was to determine the possible relationship between anterior knee pain (AKP) and nail position marked as a distance from tip of nail to tibial plateau (NP) and to the tuberositas tibiae (NT). Nail position has an influence on anterior knee pain after tibial intramedullary nailing. We evaluated postoperative outcome results of 50 patients in the last 3 years with healed fractures initially treated with intramedullary (IM) reamed nails with 2 or 3 interlocking screws on both parts of the nail and with the use of medial paratendinous incision for nail entry portal. Patients marked a point on the visual analog scale (VAS) that corresponded to the level of postoperative AKP felt. Two groups of patients were formed on the basis of AKP (pain level was neglected): groups A and B, with and without pain, respectively. The difference between the two groups concerning NP measurements was statistically significant (p<0.05), but not concerning NT measurements at the p<0.05 level. Patients were classified by pain with high accuracy (98%) according to a classification tree. Symptoms of AKP did not appear if the tip of the nail position was more than 6.0 mm from the NP and more than 2.6 mm from the NT. However, for better evaluation of these results it will be necessary to examine a larger number of postoperative patients with AKP.

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Jasminka Dobša

Papers

Modeling the growth pattern of in-season and off-season Ross 308 broiler breeder flocks

Lyrics classification using Naive Bayes

Comparison of Information Retrieval Techniques: Latent Semantic Indexing and Concept Indexing

Using Machine Learning for Web Page Classification in Search Engine Optimization

Nail position has an influence on anterior knee pain after tibial intramedullary nailing.