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

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

A morphable model for the synthesis of 3D faces

In this work, we combined both a tree and a NetVLAD (vector of locally aggregated descriptors) in order to design new deep model. In developing this model, we studied the impact of tree hyperparameters and found that branch factors had major effects on the parameter utilization as major indicator and the accuracy of the model. The new architecture presented herein exposes a novel hyperparameter called the tree branch factor, which grants additional control over model complexity and on the maps codependency. Deep Tree Net-Vector provide the flexibility to combine two very famous techniques, namely the tree-based technique and VLAD. The former reduces the number of parameters, whereas the latter provides better feature representation inside the model. This work aimed to demonstrate the integration of the strong image descriptor, VLAD, with the tree module, to gain additional control on model size, rather than obtaining better results than state-of-the-art models, and to enhance image representation inside model layers, which could then be invested towards several tasks such as image classification and retrieval. We performed experiments on the Canadian Institute for Advanced Research-10 dataset and were able to show that the proposed models were superior—in terms of information density and accuracy—to many well-known networks.

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Deep Tree Net-Vector of Locally Aggregated Descriptor (VLAD) Model

A large population around the world is suffering from voice-related complications. Computer-based voice disorder detection systems can play a substantial role in the early detection of voice disorders by providing complementary information to early-career otolaryngologists and general practitioners. However, various studies have concluded that the recording environment of voice samples affects disorder detection. This influence of the recording environment is a major obstacle in developing such systems when a local voice disorder database is not available. In addition, sometimes the number of samples is not sufficient for training the system. To overcome these issues, a blind detection system for voice disorders is designed and implemented in this study. Hence, without any prior knowledge of voice disorders, the proposed system has the ability to detect those disorders. The developed system relies only on healthy voice samples which can be recorded locally in the desired environment. The generation of a reference model for healthy subjects and decision criteria to detect voice disorders are two major tasks in the proposed systems. These tasks are implemented with two different types of speech features. Moreover, the unsupervised reference model is created by using DBSCAN and k-means algorithms. The overall performance of the system is 74.9% in terms of the geometric mean of sensitivity and specificity. The results of the proposed system are encouraging and better than the performance of Multidimensional Voice Program (MDVP) parameters which are widely used for disorder assessment by otolaryngologists in clinics.

Ghulam Muhammad

Papers

Deep Tree Net-Vector of Locally Aggregated Descriptor (VLAD) Model

Computer-based Blind Diagnostic System for Classification of Healthy and Disordered Voices