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

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

A multi-armed bandit problem - or, simply, a bandit problem - is a sequential allocation problem defined by a set of actions. At each time step, a unit resource is allocated to an action and some observable payoff is obtained. The goal is to maximize the total payoff obtained in a sequence of allocations. The name bandit refers to the colloquial term for a slot machine (a "one-armed bandit" in American slang). In a casino, a sequential allocation problem is obtained when the player is facing many slot machines at once (a "multi-armed bandit"), and must repeatedly choose where to insert the next coin. Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration-exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of bandit problems dates back to the 1930s, exploration-exploitation trade-offs arise in several modern applications, such as ad placement, website optimization, and packet routing. Mathematically, a multi-armed bandit is defined by the payoff process associated with each option. In this book, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it also analyzes some of the most important variants and extensions, such as the contextual bandit model. This monograph is an ideal reference for students and researchers with an interest in bandit problems.

Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems

In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.

Unsupervised feature selection using feature similarity

The exon deﬁnition model accounts for the paring between the 3’ splice site and its downstream 5’splice site [5]. It was demonstrated experimentally that 5’ and 3’ splice-site pairing in metazoanpre-mRNAs occurred in the two distinct steps [1]. In our previous paper, we proposed a subclassmethod to predict 5’ splice sites in mammalian pre-mRNAs, and suggested that several 5’ splice sitepattern sequences were observed more frequently on the ﬁrst or second introns than on the otherorder introns [3]. However, the relation between the 5’ splice site sequences and the order of introns iscurrently under investigation. On the other hand, positional characterization of false positives usingcomputational prediction of human splice sites was presented [6]. In the present study, by using aposition-tree method to analyze the distribution of the 5’ splice site-like sequences in human andmouse pre-mRNAs, we obtained the 5’ splice site sequences whose lengths were minimal but weresuﬃcient for specifying the 5’ splice sites. Then we investigated the distribution of the 5’ splice site-like sequences that were one nucleotide shorter than the obtained 5’ splice site sequences. As a result,it was conﬁrmed that the 5’ splice site-like sequences had the tendency of the uneven distributionwithin pre-mRNAs.

Distribution Analysis of 5' Splice Site-Like Sequences in Human and Mouse pre-mRNAs

We introduce methods for two specific-purpose Web searches. One is a search for Web communities related to given keywords, and the other is a search for texts having a certain relation to given keywords. Our methods are based on both structure and contents of WWW. Our method of Web community search uses global structure of WWW to discover communities, and uses content information to label found communities, where global structure means Web graph composed of Web pages and hyperlinks between them. On the other hand, our method of related text search uses local structure of WWW to extract candidate texts, and uses content information to filter out wrongly extracted ones, where local structure means DOM-tree structure of each page. We report the latest results on these Web search methods.

Specific-Purpose web searches on the basis of structure and contents

An information table or a training/designing sample set is all that can be obtained to infer the underlying generation mechanism (distribution) of tuples or samples However, how an information table is available in representation, in treatment, and in interpretation, can still be discussed In this paper, these matters are discussed on the basis of ldquogranularityrdquo First, an explanation is given to identify the reasons why different goals/treatments of information tables exist in some different research fields In this stage, it will be emphasized that ldquogranularity conceptrdquo plays an important role Next, a framework of information tables is reformulated in terms of attribute sets and tuple sets Here, a ldquoGalois connectionrdquo helps to understand their relationship Then, the use of ldquoclosed subsetsrdquo is proposed instead of given tuples, for efficiency and for interpretability With a special type of closed subsets, the traditional logical DNF expression framework can be naturally extended to those with multivalues and continuous values Last, several concepts on rough sets are reformulated using ldquovariable granularityrdquo connected to closed subsets This paper determines how and in what points granularity can give flexibility in dealing with several problems Through several concepts defined in this paper, some intuitions toward development of data exploration and data mining are given

Extended DNF Expression and Variable Granularity in Information Tables

Specific-purpose web searches on the basis of structure and contents

Multi-task learning (MTL) improves generalization by sharing information among related tasks. Structured sparsity-inducing regularization has been widely used in MTL to learn interpretable and compact models, especially in high-dimensional settings. These methods have achieved much success in practice, however, there are still some key limitations, such as limited generalization ability due to specific sparse constraints on parameters, usually restricted in matrix form that ignores high-order feature interactions among tasks, and formulated in various forms with different optimization algorithms. Inspired by Generalized Lasso, we propose the Generalized Group Lasso (GenGL) to overcome these limitations. In GenGL, a linear operator is introduced to make it adaptable to diverse sparsity settings, and helps it to handle hierarchical sparsity and multi-component decomposition in general tensor form, leading to enhanced flexibility and expressivity. Based on GenGL, we propose a novel framework for Structured Sparse MTL (SSMTL), that unifies a number of existing MTL methods, and implement its two new variants in shallow and deep architectures, respectively. An efficient optimization algorithm is developed to solve the unified problem, and its effectiveness is validated by synthetic and real-world experiments. 

Structured Sparse Multi-Task Learning with Generalized Group Lasso

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Mineichi Kudo

Author Tools

Chat about Author