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Machiavelli's Virtue
by Harvey C. MansfieldUniting thirty years of authoritative scholarship by a master of textual detail, Machiavelli's Virtue is a comprehensive statement on the founder of modern politics. Harvey Mansfield reveals the role of sects in Machiavelli's politics, his advice on how to rule indirectly, and the ultimately partisan character of his project, and shows him to be the founder of such modern and diverse institutions as the impersonal state and the energetic executive. Accessible and elegant, this groundbreaking interpretation explains the puzzles and reveals the ambition of Machiavelli's thought. "The book brings together essays that have mapped [Mansfield's] paths of reflection over the past thirty years. . . . The ground, one would think, is ancient and familiar, but Mansfield manages to draw out some understandings, or recognitions, jarringly new."—Hadley Arkes, New Criterion "Mansfield's book more than rewards the close reading it demands."—Colin Walters, Washington Times "[A] masterly new book on the Renaissance courtier, statesman and political philosopher. . . . Mansfield seeks to rescue Machiavelli from liberalism's anodyne rehabilitation."—Roger Kimball, The Wall Street Journal
The Machine Anxieties of Steampunk: Contemporary Philosophy, Victorian Aesthetics, and the Future
by Kathe Hicks AlbrechtWhat is steampunk and why are people across the globe eagerly embracing its neo-Victorian aesthetic? Old-fashioned eye goggles, lace corsets, leather vests, brass gears and gadgets, mechanical clocks, the look appears across popular culture, in movies, art, fashion, and literature. But steampunk is both an aesthetic program and a way-of-life and its underlying philosophy is the key to its broad appeal. Steampunk champions a new autonomy for the individual caught up in today's technology-driven society. It expresses optimism for the future but it also delivers a note of caution about our human role in a world of ever more ubiquitous and powerful machines. Thus, despite adopting an aesthetic and lifestyle straight out of the Victorian scientific romance, steampunk addresses significant 21st-century concerns about what lies ahead for humankind. The movement recovers autonomy from prevailing trends even as it challenges us to ask what it is to be human today.
The Machine Anxieties of Steampunk: Contemporary Philosophy, Victorian Aesthetics, and the Future
by Kathe Hicks AlbrechtWhat is steampunk and why are people across the globe eagerly embracing its neo-Victorian aesthetic? Old-fashioned eye goggles, lace corsets, leather vests, brass gears and gadgets, mechanical clocks, the look appears across popular culture, in movies, art, fashion, and literature. But steampunk is both an aesthetic program and a way-of-life and its underlying philosophy is the key to its broad appeal. Steampunk champions a new autonomy for the individual caught up in today's technology-driven society. It expresses optimism for the future but it also delivers a note of caution about our human role in a world of ever more ubiquitous and powerful machines. Thus, despite adopting an aesthetic and lifestyle straight out of the Victorian scientific romance, steampunk addresses significant 21st-century concerns about what lies ahead for humankind. The movement recovers autonomy from prevailing trends even as it challenges us to ask what it is to be human today.
Machine Discovery: Reprinted from Foundations of Science Volume 1, No. 2, 1995/96
by Jan ŻytkowHuman and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on searching an `instance space' (empirical exploration) and a `hypothesis space' (generation of theories). In scientific discovery, searching must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This book focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery. Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the `blackboard' of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interactions. In this sense, all research on discovery, including the investigations on individual processes discussed in this book, is social psychology, or even sociology.
Machine Ethics: From Machine Morals to the Machinery of Morality (Studies in Applied Philosophy, Epistemology and Rational Ethics #53)
by Luís Moniz Pereira António Barata LopesThis book offers the first systematic guide to machine ethics, bridging between computer science, social sciences and philosophy. Based on a dialogue between an AI scientist and a novelist philosopher, the book discusses important findings on which moral values machines can be taught and how. In turn, it investigates what kind of artificial intelligence (AI) people do actually want. What are the main consequences of the integration of AI in people’s every-day life? In order to co-exist and collaborate with humans, machines need morality, but which moral values should we teach them? Moreover, how can we implement benevolent AI? These are just some of the questions carefully examined in the book, which offers a comprehensive account of ethical issues concerning AI, on the one hand, and a timely snapshot of the power and potential benefits of this technology on the other. Starting with an introduction to common-sense ethical principles, the book then guides the reader, helping them develop and understand more complex ethical concerns and placing them in a larger, technological context. The book makes these topics accessible to a non-expert audience, while also offering alternative reading pathways to inspire more specialized readers.
Machine Ethics and Robot Ethics
by Wendell Wallach Peter AsaroOnce the stuff of science fiction, recent progress in artificial intelligence, robotics, and machine learning means that these rapidly advancing technologies are finally coming into widespread use within everyday life. Such rapid development in these areas also brings with it a host of social, political and legal issues, as well as a rise in public concern and academic interest in the ethical challenges these new technologies pose. This volume is a collection of scholarly work from leading figures in the development of both robot ethics and machine ethics; it includes essays of historical significance which have become foundational for research in these two new areas of study, as well as important recent articles. The research articles selected focus on the control and governance of computational systems; the exploration of ethical and moral theories using software and robots as laboratories or simulations; inquiry into the necessary requirements for moral agency and the basis and boundaries of rights; and questions of how best to design systems that are both useful and morally sound. Collectively the articles ask what the practical ethical and legal issues, arising from the development of robots, will be over the next twenty years and how best to address these future considerations.
Machine Ethics and Robot Ethics
by Wendell Wallach and Peter AsaroOnce the stuff of science fiction, recent progress in artificial intelligence, robotics, and machine learning means that these rapidly advancing technologies are finally coming into widespread use within everyday life. Such rapid development in these areas also brings with it a host of social, political and legal issues, as well as a rise in public concern and academic interest in the ethical challenges these new technologies pose. This volume is a collection of scholarly work from leading figures in the development of both robot ethics and machine ethics; it includes essays of historical significance which have become foundational for research in these two new areas of study, as well as important recent articles. The research articles selected focus on the control and governance of computational systems; the exploration of ethical and moral theories using software and robots as laboratories or simulations; inquiry into the necessary requirements for moral agency and the basis and boundaries of rights; and questions of how best to design systems that are both useful and morally sound. Collectively the articles ask what the practical ethical and legal issues, arising from the development of robots, will be over the next twenty years and how best to address these future considerations.
Machine Learning: 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings (Lecture Notes in Computer Science #2837)
by Nada Lavra 269 Dragan Gamberger Ljupco Todorovski Hendrik BlockeelThe proceedings of ECML/PKDD2003 are published in two volumes: the P- ceedings of the 14th European Conference on Machine Learning (LNAI 2837) and the Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2838). The two conferences were held on September 22–26, 2003 in Cavtat, a small tourist town in the vicinity of Dubrovnik, Croatia. As machine learning and knowledge discovery are two highly related ?elds, theco-locationofbothconferencesisbene?cialforbothresearchcommunities.In Cavtat, ECML and PKDD were co-located for the third time in a row, following the successful co-location of the two European conferences in Freiburg (2001) and Helsinki (2002). The co-location of ECML2003 and PKDD2003 resulted in a joint program for the two conferences, including paper presentations, invited talks, tutorials, and workshops. Out of 332 submitted papers, 40 were accepted for publication in the ECML2003proceedings,and40wereacceptedforpublicationinthePKDD2003 proceedings. All the submitted papers were reviewed by three referees. In ad- tion to submitted papers, the conference program consisted of four invited talks, four tutorials, seven workshops, two tutorials combined with a workshop, and a discovery challenge.
Machine Learning: 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings (Lecture Notes in Computer Science #2430)
by Tapio Elomaa Heikki Mannila Hannu ToivonenThis book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002.The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.
Machine Learning: 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings (Lecture Notes in Computer Science #4212)
by Johannes Fürnkranz Tobias Scheffer Myra SpiliopoulouThis book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.
Machine Learning: 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings (Lecture Notes in Computer Science #3720)
by João Gama Luís Torgo Rui Camacho Pavel Brazdil Alípio JorgeThe European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.
Machine Learning: 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings (Lecture Notes in Computer Science #4701)
by Joost N. Kok Jacek Koronacki Ramon Lopez De Mantaras Stan Matwin Dunja MladenicThis book constitutes the refereed proceedings of the 18th European Conference on Machine Learning, ECML 2007, held in Warsaw, Poland, September 2007, jointly with PKDD 2007. The 41 revised full papers and 37 revised short papers presented together with abstracts of four invited talks were carefully reviewed and selected from 592 abstracts submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
Machine Learning: 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings (Lecture Notes in Computer Science #2167)
by Luc De Raedt Peter FlachThis book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001.The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.
Machine Learning and Data Mining in Pattern Recognition: 7th International Conference, MLDM 2011, New York, NY, USA, August 30-September 3, 2011Proceedings (Lecture Notes in Computer Science #6871)
by Petra PernerThis book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.
Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012, Proceedings (Lecture Notes in Computer Science #7376)
by Petra PernerThis book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
Machine Learning and Data Mining in Pattern Recognition: 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007, Proceedings (Lecture Notes in Computer Science #4571)
by Petra PernerEver wondered what the state of the art is in machine learning and data mining? Well, now you can find out. This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections.
Machine Learning and Data Mining in Pattern Recognition: Second International Workshop, MLDM 2001, Leipzig, Germany, July 25-27, 2001. Proceedings (Lecture Notes in Computer Science #2123)
by Petra PernerThis book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001.The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval.
Machine Learning and Data Mining in Pattern Recognition: 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings (Lecture Notes in Computer Science #5632)
by Petra PernerThere is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.
Machine Learning and Data Mining in Pattern Recognition: 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings (Lecture Notes in Computer Science #3587)
by Petra Perner Atsushi ImiyaWe met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings (Lecture Notes in Computer Science #2734)
by Petra Perner Azriel RosenfeldTheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.
Machine Learning and Its Applications: Advanced Lectures (Lecture Notes in Computer Science #2049)
by Georgios Paliouras Vangelis Karkaletsis Constantine D. SpyropoulosIn recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Machine Learning and Knowledge Discovery in Databases: European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I (Lecture Notes in Computer Science #5211)
by Walter Daelemans Katharina MorikThis book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Machine Learning and Knowledge Discovery in Databases: European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II (Lecture Notes in Computer Science #5212)
by Walter Daelemans Katharina MorikThis book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part I (Lecture Notes in Computer Science #6911)
by Dimitrios Gunopulos Thomas Hofmann Donato Malerba Michalis VazirgiannisThis three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.
Machine Learning and Knowledge Discovery in Databases, Part II: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part II (Lecture Notes in Computer Science #6912)
by Dimitrios Gunopulos Thomas Hofmann Donato Malerba Michalis VazirgiannisThis three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.