From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Many large datasets associated with modern predictive data mining applications are quite complex and heterogeneous, possibly involving multiple relations, or exhibiting a dyadic nature with associated side-information. For example, one may be interested in predicting the preferences of a large set of customers for a variety of products, given various properties of both customers and products, as well as past purchase history, a social network on the customers, and a conceptual hierarchy on the products. This article provides an overview of recent innovative approaches to predictive modeling for such types of data, and also provides some concrete application scenarios to highlight the issues involved. The common philosophy in all the approaches described is to pursue a simultaneous problem decomposition and modeling strategy that can exploit heterogeneity in behavior, use the wide variety of information available and also yield relatively more interpretable solutions as compared to global ”one-shot” approaches. Since both the problem domains and approaches considered are quite new, we also highlight the potential for further investigations on several occasions throughout this article.

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Actionable Mining of Large, Multi-relational Data Using Localized Predictive Models

Equilibrium Magnetic Field Requirements during Plasma Initiation and Current Ramp-up Phase in ADITYA/ADITYA-U Tokamak Discharges

The present case study showed the novel approach of Rucaparib and Bevacizumab as first-line maintenance therapy in germline BRCA 1 mutated advanced high-grade serous carcinoma of the ovary. A 56-year-old female with high-grade serous carcinoma of the ovary (ECOG PS1) was treated with carboplatin and paclitaxel in combination with Bevacizumab (CPB), followed by interval debulking surgery. Since the patient was germline BRCA 1 positive, after completion of adjuvant chemotherapy, she was kept on Rucaparib along with Bevacizumab. The patient achieved a complete response and has been leading a disease-free life for the past one year with maintenance therapy of Rucaparib + Bevacizumab, though the patient did experience a few adverse events, including one episode of grade 3 anaemia, occasional grade 3 asthenia, and grade 2 diarrhoea (CTCAE V-4) which was managed by gradual dose reduction of Rucaparib from 600 mg twice daily dose to 300mg twice daily dose. With dose alteration of rucaparib along with bevacizumab as maintenance, the patient continues to tolerate rucaparib and stay relapse-free from disease.

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First-line Rucaparib Plus Bevacizumab Maintenance Completed One-Year in Germline BRCA1-Mutated Advanced Ovarian Cancer

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Joydeep Ghosh

Papers

Actionable Mining of Large, Multi-relational Data Using Localized Predictive Models

Equilibrium Magnetic Field Requirements during Plasma Initiation and Current Ramp-up Phase in ADITYA/ADITYA-U Tokamak Discharges

First-line Rucaparib Plus Bevacizumab Maintenance Completed One-Year in Germline BRCA1-Mutated Advanced Ovarian Cancer

Myeloid Sarcoma Presenting with Quadriparesis: A Difficult Journey through COVID Times—Lessons Learned

611P Clinical characteristics and outcomes in ultra high-risk gestational trophoblastic neoplasia: Experience from an Indian cancer center