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

International Journal of Computer and Applications

On the truck dispatching problem

Proceedings of SPIE - The International Society for Optical Engineering

The Journal of the Acoustical Society of America

paper presents a survey of the literature on writer identification schemes and techniques up till date. The paper outlines an overview of the writer identification schemes mainly in Chinese, English, Arabic and Persian languages. Taxonomy of different features adopted for online and offline writer identification schemes is also drawn at. The feature extraction methods adopted for the schemes are discussed in length outlining the merits and demerits of the same. In automated writer identification, text independent and text dependent methods are available which is also discussed in this paper. An evaluation of writer identification schemes under multiple languages is also analyzed by comparing the recognition rate.

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A Survey on Writer Identification Schemes

Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conjugate Gradient, Quick Propagation, Levenberg-Marquardt, and Delta-Bar-Delta, in predicting thunderstorms and the usefulness for the advanced prediction were studied and their performances were evaluated by a number of statistical measures. The results indicate that Levenberg-Marquardt algorithm well predicted thunderstorm affected surface parameters and 1, 3, and 24 h advanced prediction models are able to predict hourly temperature and relative humidity adequately with sudden fall and rise during thunderstorm hour. This demonstrates its distinct capability and advantages in identifying meteorological time series comprising nonlinear characteristics. The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.

/pdf/artificial-neural-network-model-in-prediction-of-39gxk6c913.pdf

Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

Numerical simulation of severe local storms over east India using WRF-NMM mesoscale model

Work on morphological analyzers (which are computer programmes) for Indian languages is conducted vigorously these days. Usually published in specialized journals, this rather technical work is briefly presented here to provide some insights to a wider readership into little-known aspects of current language work. The morphological strength of Malayalam as a major South Indian language justifies the use of thorough morphological processing, which is the first step in any natural language processing task. The project is aimed at building a morphological processor for language, with two main components: a morphological generator and a morphological analyzer. The computational model of the processor takes care of the processing of nouns, pronouns, verbs and modifiers. The results obtained are encouraging and the work can be extended to the creation of a full-fledged part-of-speech tagger for Malayalam and other Dravidian languages, since they all exhibit structural homogeneity.

A Morphological Processor for Malayalam Language

A Parts of Speech tagger for Malayalam which uses a stochastic approach has been proposed. The tagger makes use of word frequencies and bigram statistics from a corpus. The morphological analyzer is used to generate a tagged corpus due to the unavailability of an annotated corpus in Malayalam. Although the experiments have been performed on a very small corpus, the results have shown that the statistical approach works well with a highly agglutinative language like Malayalam

/pdf/development-of-a-pos-tagger-for-malayalam-an-experience-21pp61ihag.pdf

Sumam Mary Idicula

Papers

A Survey on Writer Identification Schemes

Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

Numerical simulation of severe local storms over east India using WRF-NMM mesoscale model

A Morphological Processor for Malayalam Language

Development of a POS Tagger for Malayalam - An Experience