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 MATLAB educational simulation program was developed to explore and understand standardized speech coding algorithms such as the FS-1015 LPC-10e, the FS-1016 CELP, the ETSI GSM, the IS-54 VSELP, the G.721 ADPCM, and the G.728 LD-CELP algorithms. The simulation software provides an interactive environment that allows users to display time- and frequency-domain representations of input and reconstructed speech on an IBM PC compatible equipped with a standard sound card. Scores associated with subjective and objective performance measures are computed using simulation tools. This tool is used in undergraduate and graduate DSP courses to introduce speech coding fundamentals.

A MATLAB software tool for the introduction of speech coding fundamentals in a DSP course

An investigation is presented of transform-based seismic data compression. The study concentrates on discrete orthogonal transforms such as the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the Walsh-Hadamard transform (WHT), and the Karhunen-Loeve transform (KLT). Uniform and subband transform coding schemes were implemented, and comparative results are given for data rates ranging from 150 to 550 b/s. These results are also compared to existing linear prediction techniques. >

Transform coding algorithms for seismic data compression

A block modified covariance algorithm (BMCA) is proposed for autoregressive (AR) parametric spectral estimation. The BMCA can be implemented either in the time or in the frequency domain with the latter being more efficient in high-order AR estimation. Although the BMCA operates on a block of data it allows for both block and sequential (sample-by-sample) updates. The performance of the algorithm is evaluated in terms of computational complexity and convergence characteristics. Results are given for narrowband and broadband processes. >

Block time and frequency domain modified covariance algorithms

Some embodiments include a distributed estimation system. Other embodiments of related systems and methods are also disclosed.

Energy efficient distributed estimation using nonlinear amplifiers

System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence is also considered, and their effect on Fisher information and CRB is given. The results provide a lower bound on the variance of the estimator of system size. This in turn, provides guidelines on how to choose consensus algorithms and initial values at the nodes.

Andreas Spanias

Papers

A MATLAB software tool for the introduction of speech coding fundamentals in a DSP course

Transform coding algorithms for seismic data compression

Block time and frequency domain modified covariance algorithms

Energy efficient distributed estimation using nonlinear amplifiers

Cramer-Rao Bounds for Distributed System Size Estimation Using Consensus Algorithms