There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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 - Concepts and Techniques.

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the levels of consciousness in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegative orthant of the state space for nonnegative initial conditions.

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems

The predict+optimize problem combines machine learning and
 combinatorial optimization by predicting the problem coefficients first and then using these coefficients to solve the optimization problem. 
 While this problem can be solved in two separate stages, 
 recent research shows end to end models can achieve better results. 
 This requires differentiating through a discrete combinatorial function.
 Models that use differentiable surrogates are prone to approximation errors, while existing exact models are limited to dynamic programming, or they do not generalize well with scarce data. 
 In this work we propose a novel
 divide and conquer algorithm based on transition points to reason over exact optimization problems 
 and predict
 the coefficients using the optimization loss. Moreover, our model is not limited to dynamic programming problems.
 We also introduce a greedy version, which achieves similar
 results with less computation. 
 In comparison with other predict+optimize frameworks, we show our method outperforms existing exact frameworks and can reason over hard combinatorial problems better than surrogate methods.

/pdf/a-divide-and-conquer-algorithm-for-predict-optimize-with-non-3t38qr3i.pdf

A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems

Density based logistic regression (DLR) is a recently introduced classification technique, that performs a one-to-one non-linear transformation of the original feature space to another feature space based on density estimations. This new feature space is particularly well suited for learning a logistic regression model. Whilst performance gains, good interpretability and time efficiency make DLR attractive, there exist some limitations to its formulation. In this paper, we tackle these limitations and propose several new extensions: 1) A more robust methodology for performing density estimations, 2) A method that can transform two or more features into a single target feature, based on the use of higher order kernel density estimation, 3) Analysis of the utility of DLR for transfer learning scenarios. We evaluate our extensions using several synthetic and publicly available datasets, demonstrating that higher order transformations have the potential to boost prediction performance and that DLR is a promising method for transfer learning.

Improved Feature Transformations for Classification Using Density Estimation

Evaluation of the outcome (end-product) of surgical procedures carried out in virtual reality environments is an essential part of simulation-based surgical training. Automated end-product assessment can be carried out by performance classifiers built from a set of expert performances. When applied to temporal bone surgery simulation, these classifiers can evaluate performance on the bone specimen they were trained on, but they cannot be extended to new specimens. Thus, new expert performances need to be recorded for each new specimen, requiring considerable time commitment from time-poor expert surgeons. To eliminate this need, we propose a transfer learning framework to adapt a classifier built on a single temporal bone specimen to multiple specimens. Once a classifier is trained, we translate each new specimens' features to the original feature space, which allows us to carry out performance evaluation on different specimens using the same classifier. In our experiment, we built a surgical end-product performance classifier from 16 expert trials on a simulated temporal bone specimen. We applied the transfer learning approach to 8 new specimens to obtain machine generated end-products. We also collected end-products for these 8 specimens drilled by a single expert. We then compared the machine generated end-products to those drilled by the expert. The drilled regions generated by transfer learning were similar to those drilled by the expert.

Transfer Learning of a Temporal Bone Performance Model via Anatomical Feature Registration

Classifying protein sequences has important applications in areas such as disease diagnosis, treatment development and drug design. In this paper we present a highly accurate classifier called the g-MARS (gapped Markov Chain with Support Vector Machine) protein classifier. It models the structure of a protein sequence by measuring the transition probabilities between pairs of amino acids. This results in a Markov chain style model for each protein sequence. Then, to capture the similarity among non-exactly matching protein sequences, we show that this model can be generalized to incorporate gaps in the Markov chain. We perform a thorough experimental study and compare g-MARS to several other state-of-the-art protein classifiers. Overall, we demonstrate that g-MARS has superior accuracy and operates efficiently on a diverse range of protein families.

/pdf/g-mars-protein-classification-using-gapped-markov-chains-and-5q9m2plt9u.pdf

James Bailey

Papers

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems

A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems

Improved Feature Transformations for Classification Using Density Estimation

Transfer Learning of a Temporal Bone Performance Model via Anatomical Feature Registration

g-MARS: Protein Classification Using Gapped Markov Chains and Support Vector Machines