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

Photovoltaic array topology optimization was shown to improve efficiency in renewable energy plants. Previous studies demonstrated improvements via simulation at the level of 7-12% or more. In this paper, we describe solar array topology optimization systems based on quantum machine learning algorithms. The idea of using quantum machine learning can be useful in cases where the objective is to optimize power output in large sites with several thousands of panels. We specifically propose and assess a quantum circuit for a neural network implementation for photovoltaic topology optimization. Results and comparisons are presented using classical and quantum neural network implementations. In addition, solar array topology optimization simulations and comparisons using a quantum neural network are described for different numbers of qubits.

Quantum Machine Learning for Photovoltaic Topology Optimization

A two-stage pole-zero predictor is proposed which is capable of predicting minimum phase Auto Regressive Moving Average (ARMA) processes accurately with a reduced number of parameters. This is achieved by cascading the classical predictor (lst stage), and the pole-zero recursive-like structure (2nd stage). The constraint on the order of the first stage is relaxed since the unknown process is actually represented by the parameters of the second stage. Computer simulations for both low- and high-order ARMA processes are given to demonstrate the excellent performance of the proposed technique.

A two-stage pole-zero predictor

The effective and reliable detection of explosive compounds in complex environments is an important problem in many environment and security-related applications. This paper develops an explosive detection approach based on multi-modal sensing and sensor data fusion. A least-squares feature extraction technique is designed to isolate explosive signatures in data collected using electrochemical and polymer nanojunction sensors. The information obtained from the two sensors is then efficiently combined using a Bayesian decision fusion scheme. Results are presented for the detection of the explosive compound TNT showing the merit of the proposed approach.

Least-squares based feature extraction and sensor fusion for explosive detection

An interactive learning environment for DSP

The application of block frequency domain (FFT-based) adaptive algorithms to noise cancellation is considered. Two classes of FFT-based algorithms are examined, namely those associated with periodic convolution and those associated with linear convolution. In both cases normalized optimum convergence factors are considered. Experimental noise cancellation results are given using speech corrupted by white, colored, and harmonically structured noise (simulated engine noise). Quantitative and subjective evaluations are given for all the results, and trade-offs of performance versus computational complexity are established. >

Andreas Spanias

Papers

Quantum Machine Learning for Photovoltaic Topology Optimization

A two-stage pole-zero predictor

Least-squares based feature extraction and sensor fusion for explosive detection

An interactive learning environment for DSP

Adaptive noise cancellation using fast optimum block algorithms