Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

PRODIGY is a general-purpose problem-solving architecture that serves as a basis for research in planning, machine learning, apprentice-type knowledge-refinement interfaces, and expert systems This document is a manual for the latest version of the PRODIGY system, PRODIGY40, and includes descriptions of the PRODIGY representation language, control structure, user interface, abstraction module, and other features The tutorial style is meant to provide the reader with the ability to run PRODIGY and make use of all the basic features, as well as gradually learning the more esoteric aspects of PRODIGY40

PRODIGY 4.0: The Manual and Tutorial

We research and develop autonomous mobile service robots as Collaborative Robots, i.e., CoBots. For the last three years, our four CoBots have autonomously navigated in our multi-floor office buildings for more than 1,000km, as the result of the integration of multiple perceptual, cognitive, and actuations representations and algorithms. In this paper, we identify a few core aspects of our CoBots underlying their robust functionality. The reliable mobility in the varying indoor environments comes from a novel episodic non-Markov localization. Service tasks requested by users are the input to a scheduler that can consider different types of constraints, including transfers among multiple robots. With symbiotic autonomy, the CoBots proactively seek external sources of help to fill-in for their inevitable occasional limitations. We present sampled results from a deployment and conclude with a brief review of other features of our service robots.

CoBots: robust symbiotic autonomous mobile service robots

GameBots [1] is a virtual reality platform that allows the creation and evaluation of intelligent agents that interact with a rich 3D continuous dynamic environment. As opposed to previous test beds that focus on a single task and environment (such as soccer simulation [4]), GameBots does not define a single benchmark task. Instead, the GameBots platform comes with a wide variety of predefined tasks and environments and allows anyone to extend these in various ways, or create new challenges. This enables multiagent systems (MAS) and artificial intelligence researchers to explore a wide variety of algorithms and techniques, in areas such as spatial navigation, learning, dynamic resource allocation, multiagent planning, plan-recognition, collaboration, distributed adversarial planning, and human-machine teamwork. GameBots is composed of two components. The first of these is a freely-available open source extension of the commercial Unreal Tournament game engine [3]. It defines a socket-based API allowing anyone to create agents that can participate in any Unreal Tournament games. The second component is a set of development tools, sample source code, and nonviolent graphics (replacements for the default graphics) that form a basic development environment to help users get started in using GameBots. Gal A. Kaminka, Manuela M. Veloso, Steve Schaffer,

GameBots: a flexible test bed for multiagent team research

m n oLprq_stpvu wyx!u=q_zKu={|pv}!z~zu_}{|xa}\oLo;pv}1opvn!Bq_ -n_s n_{o;u=z}s4nW>z}\oq_{ qBs6pvu wyxu^q_z@_n_(q_u={Io 4}txaq_BoL} p=W}Fs4}!z nB{p=W} q_x6xWs4q_pv}%oL}!{IoLu={W np=} pvq ot -nBs6zWR=L}ax!p\s4}6xant_{u^pvu=n_{)u~o#q x!stWxu^q_zq_stp1n4#p=}#oL}!{o;u={W x>Wq_zvz}!{_}"q_{ (q_x>Wu={|}#z}aqBs6{u^{ oLpvq_{| o"u={Fq"In o;u=pvu=n_{)pvn xtq_pvqtKWzp1n=L}6xps }txant_{|u=pvu=n_{)u={|pvn s4}aqBzR-n_stz Bn_(q_u^{o>¢¡£u^¤}!{ p=Wq_p=¥ pvnF_qBpv}L¥ (q_xLWu={|} z}aq_st{|u={W%Wq¦o {|nBp1_}>§ zu=¤!}s ̈}t#_}{|}!s<q_z£nW©;}x!p1s4}axtn6B{|u=pvu^nB{I¥-} Ks n;n oL}#q _u aR}!s<}!{|p«In_u={|pn4#q_pvpvqBxL¦¬ p=}­z}aqBs6{u^{ qBs x>Wu=pv}ax!pvsa}Lop=W}1oL}!z¤}Lo! ®"}\Wq_¤}(_}!¤}!znt}t ̄q (}!p=n!@Ln_s_u~§ s4}axpvz z}aq_st{|u={W°q_{|"xtn_ Lu={|u={W ̄q_z±_nBs6u=p=W\o u^{ q"{}! -q_"p=WqBp£u^In o;}>o1zu^pvpvz} >Ws4_}{Fn_{ n_s2>u^q¦oyLs4n_3p=W} ̄W(q_{Io u={|¤n_z¤!}a ́DWu~o z}q_s6{u^{#qBsaxLWu=pv}axpvWsa}>¥ μ|¶ ̧·­¥kq_{|(p=W}{}! s }>o;zp=o£u^p-Lstu^{ o£pvn(p=W}«s4nW>z}1n4@{|q_pvs4q_z|u=(q!_}nW©;}x!p s4}axtn6B{|u=pvu^nB{#u~o1p=W}LnLx!Bo­n41p=Wu~o1xLqtpv}!sL

PADO: a new learning architecture for object recognition

: Learning behaviors in a multiagent environment are crucial for developing and adapting multiagent systems. Reinforcement learning techniques have addressed this problem for a single agent acting in a stationary environment, which is modeled as a Markov decision process (MDP). But, multiagent environments are inherently non-stationary since the other agents are free to change their behavior as they also learn and adapt. Stochastic games, first studied in the game theory community, are a natural extension of MDPs to include multiple agents. In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques.

/pdf/an-analysis-of-stochastic-game-theory-for-multiagent-3y0mjsx0hy.pdf

Manuela Veloso

Papers

PRODIGY 4.0: The Manual and Tutorial

CoBots: robust symbiotic autonomous mobile service robots

GameBots: a flexible test bed for multiagent team research

PADO: a new learning architecture for object recognition

An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning