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

http://www.csun.edu/~andrzej/COMP529-S05/papers/Mesh%20Networks%20Survey.pdf

Wireless mesh networks: a survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A mixed Fourier-Walsh signal representation is proposed for transform coding. Basis functions from two transforms are used, namely, the discrete Fourier transform (DFT) and the Walsh-Hadamard transform (WHT). An iterative algorithm is used to implement the mixed representation. A finite number of DFT components is used to represent the selective (narrowband) dominant spectrum of speech on a frame-by-frame basis. The whitened (broadband) residual signal is represented using a small number of Walsh components. The parameters of the mixed representation are coded directly by coding the DFT and WHT components. Results are given for frame-by-frame and packet coding at rates ranging from 16 kb/s to 9.6 kb/s. >

Direct coding of a class of non-stationary signals based on mixed transforms

A new phase modeling algorithm for sinusoidal analysis and synthesis of speech signals is presented. Short-time sinusoidal phases are efficiently approximated by incorporating linear prediction, spectral sampling, delay compensation, and phase correction techniques. The algorithm is different than phase compensation methods proposed for multi-pulse LPC in that it has been tailored to sinusoidal transform coding of speech signals. Performance analysis on a large speech database indicates considerable improvement in temporal and spectral matching between the original and reconstructed signals as compared to other sinusoidal phase models as well as improved subjective quality of the reproduced speech.

A new sinusoidal phase modeling algorithm

Using graphs to represent data sets that reside on irregular and complex structures can bring special advantages. Graph signal processing (DSP G ) converts traditional DSP operators, such as time shift, linear filters and Fourier transform, from time and frequency domain to the graph domain. In machine learning applications, DSP G provides an approach for semi-supervised classification. Different from conventional graph-filter-based classifiers, we propose a new graph filter with multiple graph shift matrices that can provide better classification performance when the feature quality is uneven. To solve the resulting non-convex problem, a tight and efficient convex relaxation approach is introduced. Through a branch and bound optimization method, we can find the mapping from the optimum relaxed parameter set to original parameter set, which technically provides the globally optimum solution. Simulation experiments corroborate our results.

Global Optimization of Graph Filters with Multiple Shift Matrices

In this work we present preliminary results demonstrating the influence of electrical double layer overlap on the electromigration of polystyrene beads (PSB) through an array of 25 cylindrical nanopores. Each of the cylindrical nanopores of the array used in this study is 360nm long with a diameter of 90nm. We observe frequent Coulter events for solutions of higher ionic strength and absence of Coulter events at low ionic strength solution. At higher ionic strengths, the electric double layers in the nanopore are thin and ion transport through the nanopore follows the bulk behavior of the ionic solution. For solutions of lower ionic strength, the electric double layers are comparable to the nanopore dimensions and start to overlap, suggesting surface charge interaction with the polystyrene beads that pass through the nanopore. The work continues towards detailed statistical analysis of the characteristic events observed for different concentrations.Copyright © 2009 by ASME

Andreas Spanias

Papers

Direct coding of a class of non-stationary signals based on mixed transforms

A new sinusoidal phase modeling algorithm

Global Optimization of Graph Filters with Multiple Shift Matrices

Electromigration of Charged Polystyrene Beads Through Silicon Nanopores Filled With Low Ionic Strength Solutions

On the use of directed transfer function for nonlinear systems