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

In this paper, we describe the gradient-with-projection-on-optimal-bounding-ellipsoid (G-Probe) algorithm, for use in channel equalization. The G-Probe is compared to the NLMS-based equalizer in the case where the transmitted signal is attenuated by strong and periodic fading, and the channel undergoes structured interference at the same time. In such scenarios small perturbations in the channel may cause the NLMS parameters to drift, leading to large instantaneous errors referred to as burst phenomena. The G-Probe provides both MSE and instantaneous steady-state error performance guarantees resulting in reduced error bursts. This paper provides simulation results in the performance of G-Probe for a linear ISI channel with AWGN and its performance in a channel with periodic fading. Finally, the paper provides comparison results of G-Probe against three adaptive algorithms; the OBE, NLMS, and RLS.

Channel equalization using the G-Probe

Fast adaptive algorithms using eigenspace projections

This paper addresses the problem of localizing a speech source in a reverberant environment. Simulations and real-time experiments are conducted and several methods are examined including the traditional steered-response-power (SRP) and the delay-and-sum beamformer (DSB). An algorithm that uses a particle filter in conjunction with an adaptive beamformer is developed, analyzed, and tested in a reverberant environment. The use of the particle filter provided improved results, relative to traditional DSB localization methods, in offline simulations and real-time experiments.

Adaptive beamforming and particle filtering

An optimum block modified covariance algorithm is developed for computing time-varying autoregressive (AR) parameters. The method presented differs from those presented previously in that it uses optimally selected time-varying convergence factors such that the block mean square error is minimized from one iteration to the next. In particular, the algorithm developed here, called block modified covariance algorithm with individual adaptation of parameters (BMCAI), uses individual time-varying convergence factors computed using modified covariance matrix approximations along with the Gauss-Seidel method. Even though the BMCAI is gradient based it retains the attractive spectral matching properties of fixed-window least squares modified covariance algorithms while at the same time providing capabilities for time-varying spectral estimation.

An adaptive modified covariance algorithm for spectral analysis

Java-DSP (J-DSP) is a free online Java applet that has been extensively used in signal processing education and research. We present the functionalities of J-DSP Earth Systems Edition (J-DSP/ESE) that uses the basic architecture of J-DSP, but has functions tailor-made for Earth systems signals. No text-based programming is required, so that users can focus on understanding signal processing concepts. Here, we describe the functionalities in the current version of J-DSP/ESE. A coherency analysis of Earth time series is presented. In order to overcome the inherent limitations of J-DSP/ESE in terms of memory and computations, a standalone Java application is proposed. This will greatly enhance the functionalities of the existing J-DSP/ESE applet. The standalone application will be platform independent and available for free. These additional functionalities of the application make it suitable for use in research as well as education.

Andreas Spanias

Papers

Channel equalization using the G-Probe

Fast adaptive algorithms using eigenspace projections

Adaptive beamforming and particle filtering

An adaptive modified covariance algorithm for spectral analysis

Work in progress: The J-DSP/ESE software for analyzing Earth systems signals