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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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

Unsupervised neural machine translation (NMT) utilizes only monolingual data for training. The quality of back-translated data plays an important role in the performance of NMT systems. In back-translation, all generated pseudo parallel sentence pairs are not of the same quality. Taking inspiration from domain adaptation where in-domain sentences are given more weight in training, in this paper we propose an approach to filter back-translated data as part of the training process of unsupervised NMT. Our approach gives more weight to good pseudo parallel sentence pairs in the back-translation phase. We calculate the weight of each pseudo parallel sentence pair using sentence-wise round-trip BLEU score which is normalized batch-wise. We compare our approach with the current state of the art approaches for unsupervised NMT.

/pdf/filtering-back-translated-data-in-unsupervised-neural-4gdyu3n93q.pdf

Filtering Back-Translated Data in Unsupervised Neural Machine Translation

Editorial pre-screening is the first step in academic peer review. The deluge of research papers and the huge amount of submissions being made to journals these days makes editorial decision a very challenging task. The current work attempts to investigate certain impact factors that may have a role in the editorial decision making process. The proposed work exhibits potential for the development of an AI-assisted peer review system which could aid the editors as well as the authors in making appropriate decisions in reasonable time and thus accelerate the overall process of scholarly publishing.

Investigating Impact Features in Editorial Pre-Screening of Research Papers

The current paper reports about the development of an automatic clustering technique which builds upon the search capability of a self-organizing multi-objective differential evolutionary approach. The algorithm utilizes new search operators which are developed after considering the neighbor-hood relationships of solutions of a population extracted using a self organizing map (SOM). Variable number of cluster centers are encoded in different solutions of the population which are evolved using the new search operators of differential evolution to automatically determine the number of clusters. Two cluster validity indices capturing different goodness measures of partitioning are used as objective functions. The effectiveness of the proposed framework namely, self organizing map based multi-objective (MO) clustering technique (SMEA_clust) is shown for automatically partitioning four artificial and four real-life data sets in comparison with a multi-objective differential evolution based clustering technique (similar to our proposed approach but without using SOM concept), two recent multi-objective clustering based techniques, VAMOSA and MOCK. Results are further validated using statistical significance tests.

A Self Organizing Map Based Multi-objective Framework for Automatic Evolution of Clusters

Image captioning in Hindi language using transformer networks

Measuring semantic nearness of documents is important for accurate information retrieval, automated text categorization and classification. Inspired by the observation that text documents contain semantically coherent set of ideas/topics, this paper presents the design and experimental evaluation of a method to represent a text document as a set of concepts. Based on this, we propose a method to measure semantic nearness of texts. Our method makes use of WordNet which is a lexico-semantic network of words. We bypass word sense disambiguation. In order to show the effectiveness of our representation of texts, we compare experimental results of text classification and clustering with the results of classification and clustering with standard techniques.

https://link.springer.com/content/pdf/10.1007%2F11590316_109.pdf

Pushpak Bhattacharyya

Papers

Filtering Back-Translated Data in Unsupervised Neural Machine Translation

Investigating Impact Features in Editorial Pre-Screening of Research Papers

A Self Organizing Map Based Multi-objective Framework for Automatic Evolution of Clusters

Image captioning in Hindi language using transformer networks

Text similarity measurement using concept representation of texts