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

IEEE Transactions on Pattern Analysis and Machine Intelligence

The terms 'antioxidant', 'oxidative stress' and 'oxidative damage' are widely used but rarely defined. This brief review attempts to define them and to examine the ways in which oxidative stress and oxidative damage can affect cell behaviour both in vivo and in cell culture, using cancer as an example.

Biochemistry of oxidative stress

Time series analysis and its applications

https://www.math.unipd.it/~aiolli/PAPERS/donini_neurocomputing15.pdf

EasyMKL: a scalable multiple kernel learning algorithm

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

We present a simple and scalable algorithm for top-N recommendation able to deal with very large datasets and (binary rated) implicit feedback. We focus on memory-based collaborative filtering algorithms similar to the well known neighboor based technique for explicit feedback. The major difference, that makes the algorithm particularly scalable, is that it uses positive feedback only and no explicit computation of the complete (user-by-user or item-by-item) similarity matrix needs to be performed.The study of the proposed algorithm has been conducted on data from the Million Songs Dataset (MSD) challenge whose task was to suggest a set of songs (out of more than 380k available songs) to more than 100k users given half of the user listening history and complete listening history of other 1 million people.In particular, we investigate on the entire recommendation pipeline, starting from the definition of suitable similarity and scoring functions and suggestions on how to aggregate multiple ranking strategies to define the overall recommendation. The technique we are proposing extends and improves the one that already won the MSD challenge last year.

/pdf/efficient-top-n-recommendation-for-very-large-scale-binary-154w0eiqx7.pdf

Efficient top-n recommendation for very large scale binary rated datasets

Winner-take-all multiclass classifiers are built on the top of a set of prototypes each representing one of the available classes. A pattern is then classified with the label associated to the most 'similar' prototype. Recent proposal of SVM extensions to multiclass can be considered instances of the same strategy with one prototype per class.The multi-prototype SVM proposed in this paper extends multiclass SVM to multiple prototypes per class. It allows to combine several vectors in a principled way to obtain large margin decision functions. For this problem, we give a compact constrained quadratic formulation and we propose a greedy optimization algorithm able to find locally optimal solutions for the non convex objective function.This algorithm proceeds by reducing the overall problem into a series of simpler convex problems. For the solution of these reduced problems an efficient optimization algorithm is proposed. A number of pattern selection strategies are then discussed to speed-up the optimization process. In addition, given the combinatorial nature of the overall problem, stochastic search strategies are suggested to escape from local minima which are not globally optimal.Finally, we report experiments on a number of datasets. The performance obtained using few simple linear prototypes is comparable to that obtained by state-of-the-art kernel-based methods but with a significant reduction (of one or two orders) in response time.

/pdf/multiclass-classification-with-multi-prototype-support-1tmdqqkgty.pdf

Multiclass Classification with Multi-Prototype Support Vector Machines

Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier. The same principle has been already used in SVM and other kernel based methods as the associated optimization problems try to maximize the minimum of these margins.

In this paper, we propose a kernel based method for the direct optimization of the margin distribution (KM-OMD). The method is motivated and analyzed from a game theoretical perspective. A quite efficient optimization algorithm is then proposed. Experimental results over a standard benchmark of 13 datasets have clearly shown state-of-the-art performances.

/pdf/a-kernel-method-for-the-optimization-of-the-margin-20q4z7ukpn.pdf

Fabio Aiolli

Papers

EasyMKL: a scalable multiple kernel learning algorithm

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

Efficient top-n recommendation for very large scale binary rated datasets

Multiclass Classification with Multi-Prototype Support Vector Machines

A Kernel Method for the Optimization of the Margin Distribution