Academic literature on the topic 'Large margin classifiers'

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Journal articles on the topic "Large margin classifiers"

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Leite, Saul C., and Raul Fonseca Neto. "Incremental margin algorithm for large margin classifiers." Neurocomputing 71, no. 7-9 (March 2008): 1550–60. http://dx.doi.org/10.1016/j.neucom.2007.05.002.

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Qi, Zhengling, and Yufeng Liu. "Convex Bidirectional Large Margin Classifiers." Technometrics 61, no. 2 (September 12, 2018): 176–86. http://dx.doi.org/10.1080/00401706.2018.1497544.

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Wu, Yichao, and Yufeng Liu. "Adaptively Weighted Large Margin Classifiers." Journal of Computational and Graphical Statistics 22, no. 2 (April 2013): 416–32. http://dx.doi.org/10.1080/10618600.2012.680866.

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Domeniconi, C., D. Gunopulos, and J. Peng. "Large Margin Nearest Neighbor Classifiers." IEEE Transactions on Neural Networks 16, no. 4 (July 2005): 899–909. http://dx.doi.org/10.1109/tnn.2005.849821.

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Wang, J., X. Shen, and Y. Liu. "Probability estimation for large-margin classifiers." Biometrika 95, no. 1 (January 31, 2008): 149–67. http://dx.doi.org/10.1093/biomet/asm077.

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Gottlieb, Lee-Ad, Eran Kaufman, and Aryeh Kontorovich. "Apportioned margin approach for cost sensitive large margin classifiers." Annals of Mathematics and Artificial Intelligence 89, no. 12 (October 8, 2021): 1215–35. http://dx.doi.org/10.1007/s10472-021-09776-w.

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Fu, Sheng, Sanguo Zhang, and Yufeng Liu. "Adaptively weighted large-margin angle-based classifiers." Journal of Multivariate Analysis 166 (July 2018): 282–99. http://dx.doi.org/10.1016/j.jmva.2018.03.004.

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Cevikalp, Hakan, Bill Triggs, Hasan Serhan Yavuz, Yalçın Küçük, Mahide Küçük, and Atalay Barkana. "Large margin classifiers based on affine hulls." Neurocomputing 73, no. 16-18 (October 2010): 3160–68. http://dx.doi.org/10.1016/j.neucom.2010.06.018.

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Kaizhu Huang, Haiqin Yang, I. King, and M. R. Lyu. "Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally." IEEE Transactions on Neural Networks 19, no. 2 (February 2008): 260–72. http://dx.doi.org/10.1109/tnn.2007.905855.

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Bermejo, Sergio, and Joan Cabestany. "Oriented principal component analysis for large margin classifiers." Neural Networks 14, no. 10 (December 2001): 1447–61. http://dx.doi.org/10.1016/s0893-6080(01)00106-x.

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Dissertations / Theses on the topic "Large margin classifiers"

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Tsampouka, Petroula. "Perceptron-like large margin classifiers." Thesis, University of Southampton, 2007. https://eprints.soton.ac.uk/264242/.

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We address the problem of binary linear classification with emphasis on algorithms that lead to separation of the data with large margins. We motivate large margin classification from statistical learning theory and review two broad categories of large margin classifiers, namely Support Vector Machines which operate in a batch setting and Perceptron-like algorithms which operate in an incremental setting and are driven by their mistakes. We subsequently examine in detail the class of Perceptron-like large margin classifiers. The algorithms belonging to this category are further classified on the basis of criteria such as the type of the misclassification condition or the behaviour of the effective learning rate, i.e. the ratio of the learning rate to the length of the weight vector, as a function of the number of mistakes. Moreover, their convergence is examined with a prominent role in such an investigation played by the notion of stepwise convergence which offers the possibility of a rather unified approach. Whenever possible, mistake bounds implying convergence in a finite number of steps are derived and discussed. Two novel families of approximate maximum margin algorithms called CRAMMA and MICRA are introduced and analysed theoretically. In addition, in order to deal with linearly inseparable data a soft margin approach for Perceptron-like large margin classifiers is discussed. Finally, a series of experiments on artificial as well as real-world data employing the newly introduced algorithms are conducted allowing a detailed comparative assessment of their performance with respect to other well-known Perceptron-like large margin classifiers and state-of-the-art Support Vector Machines.
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Sassano, Manabu. "Practical Use of Large Margin Classifiers in Natural Language Processing." 京都大学 (Kyoto University), 2008. http://hdl.handle.net/2433/123820.

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Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.

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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
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TU, SHANSHAN. "Case Influence and Model Complexity in Regression and Classification." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563324139376977.

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Harrington, Edward. "Aspects of Online Learning." Phd thesis, 2004. http://hdl.handle.net/1885/47147.

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Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...
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Books on the topic "Large margin classifiers"

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Smola, Alexander J., Peter Bartlett, Bernhard Schölkopf, and Dale Schuurmans, eds. Advances in Large-Margin Classifiers. The MIT Press, 2000. http://dx.doi.org/10.7551/mitpress/1113.001.0001.

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(Editor), Peter J. Bartlett, Bernhard Schölkopf (Editor), Dale Schuurmans (Editor), and Alex J. Smola (Editor), eds. Advances in Large-Margin Classifiers (Neural Information Processing). The MIT Press, 2000.

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Johansen, Bruce, and Adebowale Akande, eds. Nationalism: Past as Prologue. Nova Science Publishers, Inc., 2021. http://dx.doi.org/10.52305/aief3847.

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Nationalism: Past as Prologue began as a single volume being compiled by Ad Akande, a scholar from South Africa, who proposed it to me as co-author about two years ago. The original idea was to examine how the damaging roots of nationalism have been corroding political systems around the world, and creating dangerous obstacles for necessary international cooperation. Since I (Bruce E. Johansen) has written profusely about climate change (global warming, a.k.a. infrared forcing), I suggested a concerted effort in that direction. This is a worldwide existential threat that affects every living thing on Earth. It often compounds upon itself, so delays in reducing emissions of fossil fuels are shortening the amount of time remaining to eliminate the use of fossil fuels to preserve a livable planet. Nationalism often impedes solutions to this problem (among many others), as nations place their singular needs above the common good. Our initial proposal got around, and abstracts on many subjects arrived. Within a few weeks, we had enough good material for a 100,000-word book. The book then fattened to two moderate volumes and then to four two very hefty tomes. We tried several different titles as good submissions swelled. We also discovered that our best contributors were experts in their fields, which ranged the world. We settled on three stand-alone books:” 1/ nationalism and racial justice. Our first volume grew as the growth of Black Lives Matter following the brutal killing of George Floyd ignited protests over police brutality and other issues during 2020, following the police assassination of Floyd in Minneapolis. It is estimated that more people took part in protests of police brutality during the summer of 2020 than any other series of marches in United States history. This includes upheavals during the 1960s over racial issues and against the war in Southeast Asia (notably Vietnam). We choose a volume on racism because it is one of nationalism’s main motive forces. This volume provides a worldwide array of work on nationalism’s growth in various countries, usually by authors residing in them, or in the United States with ethnic ties to the nation being examined, often recent immigrants to the United States from them. Our roster of contributors comprises a small United Nations of insightful, well-written research and commentary from Indonesia, New Zealand, Australia, China, India, South Africa, France, Portugal, Estonia, Hungary, Russia, Poland, Kazakhstan, Georgia, and the United States. Volume 2 (this one) describes and analyzes nationalism, by country, around the world, except for the United States; and 3/material directly related to President Donald Trump, and the United States. The first volume is under consideration at the Texas A & M University Press. The other two are under contract to Nova Science Publishers (which includes social sciences). These three volumes may be used individually or as a set. Environmental material is taken up in appropriate places in each of the three books. * * * * * What became the United States of America has been strongly nationalist since the English of present-day Massachusetts and Jamestown first hit North America’s eastern shores. The country propelled itself across North America with the self-serving ideology of “manifest destiny” for four centuries before Donald Trump came along. Anyone who believes that a Trumpian affection for deportation of “illegals” is a new thing ought to take a look at immigration and deportation statistics in Adam Goodman’s The Deportation Machine: America’s Long History of Deporting Immigrants (Princeton University Press, 2020). Between 1920 and 2018, the United States deported 56.3 million people, compared with 51.7 million who were granted legal immigration status during the same dates. Nearly nine of ten deportees were Mexican (Nolan, 2020, 83). This kind of nationalism, has become an assassin of democracy as well as an impediment to solving global problems. Paul Krugman wrote in the New York Times (2019:A-25): that “In their 2018 book, How Democracies Die, the political scientists Steven Levitsky and Daniel Ziblatt documented how this process has played out in many countries, from Vladimir Putin’s Russia, to Recep Erdogan’s Turkey, to Viktor Orban’s Hungary. Add to these India’s Narendra Modi, China’s Xi Jinping, and the United States’ Donald Trump, among others. Bit by bit, the guardrails of democracy have been torn down, as institutions meant to serve the public became tools of ruling parties and self-serving ideologies, weaponized to punish and intimidate opposition parties’ opponents. On paper, these countries are still democracies; in practice, they have become one-party regimes….And it’s happening here [the United States] as we speak. If you are not worried about the future of American democracy, you aren’t paying attention” (Krugmam, 2019, A-25). We are reminded continuously that the late Carl Sagan, one of our most insightful scientific public intellectuals, had an interesting theory about highly developed civilizations. Given the number of stars and planets that must exist in the vast reaches of the universe, he said, there must be other highly developed and organized forms of life. Distance may keep us from making physical contact, but Sagan said that another reason we may never be on speaking terms with another intelligent race is (judging from our own example) could be their penchant for destroying themselves in relatively short order after reaching technological complexity. This book’s chapters, introduction, and conclusion examine the worldwide rise of partisan nationalism and the damage it has wrought on the worldwide pursuit of solutions for issues requiring worldwide scope, such scientific co-operation public health and others, mixing analysis of both. We use both historical description and analysis. This analysis concludes with a description of why we must avoid the isolating nature of nationalism that isolates people and encourages separation if we are to deal with issues of world-wide concern, and to maintain a sustainable, survivable Earth, placing the dominant political movement of our time against the Earth’s existential crises. Our contributors, all experts in their fields, each have assumed responsibility for a country, or two if they are related. This work entwines themes of worldwide concern with the political growth of nationalism because leaders with such a worldview are disinclined to co-operate internationally at a time when nations must find ways to solve common problems, such as the climate crisis. Inability to cooperate at this stage may doom everyone, eventually, to an overheated, stormy future plagued by droughts and deluges portending shortages of food and other essential commodities, meanwhile destroying large coastal urban areas because of rising sea levels. Future historians may look back at our time and wonder why as well as how our world succumbed to isolating nationalism at a time when time was so short for cooperative intervention which is crucial for survival of a sustainable earth. Pride in language and culture is salubrious to individuals’ sense of history and identity. Excess nationalism that prevents international co-operation on harmful worldwide maladies is quite another. As Pope Francis has pointed out: For all of our connectivity due to expansion of social media, ability to communicate can breed contempt as well as mutual trust. “For all our hyper-connectivity,” said Francis, “We witnessed a fragmentation that made it more difficult to resolve problems that affect us all” (Horowitz, 2020, A-12). The pope’s encyclical, titled “Brothers All,” also said: “The forces of myopic, extremist, resentful, and aggressive nationalism are on the rise.” The pope’s document also advocates support for migrants, as well as resistance to nationalist and tribal populism. Francis broadened his critique to the role of market capitalism, as well as nationalism has failed the peoples of the world when they need co-operation and solidarity in the face of the world-wide corona virus pandemic. Humankind needs to unite into “a new sense of the human family [Fratelli Tutti, “Brothers All”], that rejects war at all costs” (Pope, 2020, 6-A). Our journey takes us first to Russia, with the able eye and honed expertise of Richard D. Anderson, Jr. who teaches as UCLA and publishes on the subject of his chapter: “Putin, Russian identity, and Russia’s conduct at home and abroad.” Readers should find Dr. Anderson’s analysis fascinating because Vladimir Putin, the singular leader of Russian foreign and domestic policy these days (and perhaps for the rest of his life, given how malleable Russia’s Constitution has become) may be a short man physically, but has high ambitions. One of these involves restoring the old Russian (and Soviet) empire, which would involve re-subjugating a number of nations that broke off as the old order dissolved about 30 years ago. President (shall we say czar?) Putin also has international ambitions, notably by destabilizing the United States, where election meddling has become a specialty. The sight of Putin and U.S. president Donald Trump, two very rich men (Putin $70-$200 billion; Trump $2.5 billion), nuzzling in friendship would probably set Thomas Jefferson and Vladimir Lenin spinning in their graves. The road of history can take some unanticipated twists and turns. Consider Poland, from which we have an expert native analysis in chapter 2, Bartosz Hlebowicz, who is a Polish anthropologist and journalist. His piece is titled “Lawless and Unjust: How to Quickly Make Your Own Country a Puppet State Run by a Group of Hoodlums – the Hopeless Case of Poland (2015–2020).” When I visited Poland to teach and lecture twice between 2006 and 2008, most people seemed to be walking on air induced by freedom to conduct their own affairs to an unusual degree for a state usually squeezed between nationalists in Germany and Russia. What did the Poles then do in a couple of decades? Read Hlebowicz’ chapter and decide. It certainly isn’t soft-bellied liberalism. In Chapter 3, with Bruce E. Johansen, we visit China’s western provinces, the lands of Tibet as well as the Uighurs and other Muslims in the Xinjiang region, who would most assuredly resent being characterized as being possessed by the Chinese of the Han to the east. As a student of Native American history, I had never before thought of the Tibetans and Uighurs as Native peoples struggling against the Independence-minded peoples of a land that is called an adjunct of China on most of our maps. The random act of sitting next to a young woman on an Air India flight out of Hyderabad, bound for New Delhi taught me that the Tibetans had something to share with the Lakota, the Iroquois, and hundreds of other Native American states and nations in North America. Active resistance to Chinese rule lasted into the mid-nineteenth century, and continues today in a subversive manner, even in song, as I learned in 2018 when I acted as a foreign adjudicator on a Ph.D. dissertation by a Tibetan student at the University of Madras (in what is now in a city called Chennai), in southwestern India on resistance in song during Tibet’s recent history. Tibet is one of very few places on Earth where a young dissident can get shot to death for singing a song that troubles China’s Quest for Lebensraum. The situation in Xinjiang region, where close to a million Muslims have been interned in “reeducation” camps surrounded with brick walls and barbed wire. They sing, too. Come with us and hear the music. Back to Europe now, in Chapter 4, to Portugal and Spain, we find a break in the general pattern of nationalism. Portugal has been more progressive governmentally than most. Spain varies from a liberal majority to military coups, a pattern which has been exported to Latin America. A situation such as this can make use of the term “populism” problematic, because general usage in our time usually ties the word into a right-wing connotative straightjacket. “Populism” can be used to describe progressive (left-wing) insurgencies as well. José Pinto, who is native to Portugal and also researches and writes in Spanish as well as English, in “Populism in Portugal and Spain: a Real Neighbourhood?” provides insight into these historical paradoxes. Hungary shares some historical inclinations with Poland (above). Both emerged from Soviet dominance in an air of developing freedom and multicultural diversity after the Berlin Wall fell and the Soviet Union collapsed. Then, gradually at first, right wing-forces began to tighten up, stripping structures supporting popular freedom, from the courts, mass media, and other institutions. In Chapter 5, Bernard Tamas, in “From Youth Movement to Right-Liberal Wing Authoritarianism: The Rise of Fidesz and the Decline of Hungarian Democracy” puts the renewed growth of political and social repression into a context of worldwide nationalism. Tamas, an associate professor of political science at Valdosta State University, has been a postdoctoral fellow at Harvard University and a Fulbright scholar at the Central European University in Budapest, Hungary. His books include From Dissident to Party Politics: The Struggle for Democracy in Post-Communist Hungary (2007). Bear in mind that not everyone shares Orbán’s vision of what will make this nation great, again. On graffiti-covered walls in Budapest, Runes (traditional Hungarian script) has been found that read “Orbán is a motherfucker” (Mikanowski, 2019, 58). Also in Europe, in Chapter 6, Professor Ronan Le Coadic, of the University of Rennes, Rennes, France, in “Is There a Revival of French Nationalism?” Stating this title in the form of a question is quite appropriate because France’s nationalistic shift has built and ebbed several times during the last few decades. For a time after 2000, it came close to assuming the role of a substantial minority, only to ebb after that. In 2017, the candidate of the National Front reached the second round of the French presidential election. This was the second time this nationalist party reached the second round of the presidential election in the history of the Fifth Republic. In 2002, however, Jean-Marie Le Pen had only obtained 17.79% of the votes, while fifteen years later his daughter, Marine Le Pen, almost doubled her father's record, reaching 33.90% of the votes cast. Moreover, in the 2019 European elections, re-named Rassemblement National obtained the largest number of votes of all French political formations and can therefore boast of being "the leading party in France.” The brutality of oppressive nationalism may be expressed in personal relationships, such as child abuse. While Indonesia and Aotearoa [the Maoris’ name for New Zealand] hold very different ranks in the United Nations Human Development Programme assessments, where Indonesia is classified as a medium development country and Aotearoa New Zealand as a very high development country. In Chapter 7, “Domestic Violence Against Women in Indonesia and Aotearoa New Zealand: Making Sense of Differences and Similarities” co-authors, in Chapter 8, Mandy Morgan and Dr. Elli N. Hayati, from New Zealand and Indonesia respectively, found that despite their socio-economic differences, one in three women in each country experience physical or sexual intimate partner violence over their lifetime. In this chapter ther authors aim to deepen understandings of domestic violence through discussion of the socio-economic and demographic characteristics of theit countries to address domestic violence alongside studies of women’s attitudes to gender norms and experiences of intimate partner violence. One of the most surprising and upsetting scholarly journeys that a North American student may take involves Adolf Hitler’s comments on oppression of American Indians and Blacks as he imagined the construction of the Nazi state, a genesis of nationalism that is all but unknown in the United States of America, traced in this volume (Chapter 8) by co-editor Johansen. Beginning in Mein Kampf, during the 1920s, Hitler explicitly used the westward expansion of the United States across North America as a model and justification for Nazi conquest and anticipated colonization by Germans of what the Nazis called the “wild East” – the Slavic nations of Poland, the Baltic states, Ukraine, and Russia, most of which were under control of the Soviet Union. The Volga River (in Russia) was styled by Hitler as the Germans’ Mississippi, and covered wagons were readied for the German “manifest destiny” of imprisoning, eradicating, and replacing peoples the Nazis deemed inferior, all with direct references to events in North America during the previous century. At the same time, with no sense of contradiction, the Nazis partook of a long-standing German romanticism of Native Americans. One of Goebbels’ less propitious schemes was to confer honorary Aryan status on Native American tribes, in the hope that they would rise up against their oppressors. U.S. racial attitudes were “evidence [to the Nazis] that America was evolving in the right direction, despite its specious rhetoric about equality.” Ming Xie, originally from Beijing, in the People’s Republic of China, in Chapter 9, “News Coverage and Public Perceptions of the Social Credit System in China,” writes that The State Council of China in 2014 announced “that a nationwide social credit system would be established” in China. “Under this system, individuals, private companies, social organizations, and governmental agencies are assigned a score which will be calculated based on their trustworthiness and daily actions such as transaction history, professional conduct, obedience to law, corruption, tax evasion, and academic plagiarism.” The “nationalism” in this case is that of the state over the individual. China has 1.4 billion people; this system takes their measure for the purpose of state control. Once fully operational, control will be more subtle. People who are subject to it, through modern technology (most often smart phones) will prompt many people to self-censor. Orwell, modernized, might write: “Your smart phone is watching you.” Ming Xie holds two Ph.Ds, one in Public Administration from University of Nebraska at Omaha and another in Cultural Anthropology from the Chinese Academy of Social Sciences, Beijing, where she also worked for more than 10 years at a national think tank in the same institution. While there she summarized news from non-Chinese sources for senior members of the Chinese Communist Party. Ming is presently an assistant professor at the Department of Political Science and Criminal Justice, West Texas A&M University. In Chapter 10, analyzing native peoples and nationhood, Barbara Alice Mann, Professor of Honours at the University of Toledo, in “Divide, et Impera: The Self-Genocide Game” details ways in which European-American invaders deprive the conquered of their sense of nationhood as part of a subjugation system that amounts to genocide, rubbing out their languages and cultures -- and ultimately forcing the native peoples to assimilate on their own, for survival in a culture that is foreign to them. Mann is one of Native American Studies’ most acute critics of conquests’ contradictions, and an author who retrieves Native history with a powerful sense of voice and purpose, having authored roughly a dozen books and numerous book chapters, among many other works, who has traveled around the world lecturing and publishing on many subjects. Nalanda Roy and S. Mae Pedron in Chapter 11, “Understanding the Face of Humanity: The Rohingya Genocide.” describe one of the largest forced migrations in the history of the human race, the removal of 700,000 to 800,000 Muslims from Buddhist Myanmar to Bangladesh, which itself is already one of the most crowded and impoverished nations on Earth. With about 150 million people packed into an area the size of Nebraska and Iowa (population less than a tenth that of Bangladesh, a country that is losing land steadily to rising sea levels and erosion of the Ganges river delta. The Rohingyas’ refugee camp has been squeezed onto a gigantic, eroding, muddy slope that contains nearly no vegetation. However, Bangladesh is majority Muslim, so while the Rohingya may starve, they won’t be shot to death by marauding armies. Both authors of this exquisite (and excruciating) account teach at Georgia Southern University in Savannah, Georgia, Roy as an associate professor of International Studies and Asian politics, and Pedron as a graduate student; Roy originally hails from very eastern India, close to both Myanmar and Bangladesh, so he has special insight into the context of one of the most brutal genocides of our time, or any other. This is our case describing the problems that nationalism has and will pose for the sustainability of the Earth as our little blue-and-green orb becomes more crowded over time. The old ways, in which national arguments often end in devastating wars, are obsolete, given that the Earth and all the people, plants, and other animals that it sustains are faced with the existential threat of a climate crisis that within two centuries, more or less, will flood large parts of coastal cities, and endanger many species of plants and animals. To survive, we must listen to the Earth, and observe her travails, because they are increasingly our own.
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Book chapters on the topic "Large margin classifiers"

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Bermejo, Sergio, and Joan Cabestany. "Large Margin Nearest Neighbor Classifiers." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, 669–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_80.

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Sha, Fei, Lawrence K. Saul, and Daniel D. Lee. "Multiplicative Updates for Large Margin Classifiers." In Learning Theory and Kernel Machines, 188–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_15.

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Semerci, Murat, and Ethem Alpaydın. "Mixtures of Large Margin Nearest Neighbor Classifiers." In Advanced Information Systems Engineering, 675–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40991-2_43.

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Tsampouka, Petroula, and John Shawe-Taylor. "Analysis of Generic Perceptron-Like Large Margin Classifiers." In Machine Learning: ECML 2005, 750–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564096_77.

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Liu, Mingzeng, and Yuanhai Shao. "Large Margin Proximal Non-parallel Support Vector Classifiers." In Lecture Notes in Computer Science, 715–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93713-7_69.

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Neumann, Julia, Christoph Schnörr, and Gabriele Steidl. "Feasible Adaptation Criteria for Hybrid Wavelet – Large Margin Classifiers." In Computer Analysis of Images and Patterns, 588–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_72.

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Pernkopf, Franz, and Michael Wohlmayr. "Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models." In Machine Learning and Knowledge Discovery in Databases, 50–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8_4.

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Dogan, Ürün, Tobias Glasmachers, and Christian Igel. "A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes." In Machine Learning and Knowledge Discovery in Databases, 122–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_13.

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Takamura, Hiroya, and Manabu Okumura. "A Comparative Study on the Use of Labeled and Unlabeled Data for Large Margin Classifiers." In Natural Language Processing – IJCNLP 2004, 456–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_48.

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Liu, Xinwang, Jianping Yin, En Zhu, Guomin Zhang, Yubin Zhan, and Miaomiao Li. "A Large Margin Classifier with Additional Features." In Machine Learning and Data Mining in Pattern Recognition, 82–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_7.

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Conference papers on the topic "Large margin classifiers"

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Wu, Mingrui, Bernhard Schölkopf, and Gökhan Bakir. "Building Sparse Large Margin Classifiers." In the 22nd international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102477.

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Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael R. Lyu. "Learning large margin classifiers locally and globally." In Twenty-first international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1015330.1015365.

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Cevikalp, Hakan, and Bill Triggs. "Large margin classifiers based on convex class models." In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccvw.2009.5457713.

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Qing, Xiang-Yun, Peng Ding, and Xing-Yu Wang. "Learning large margin nearest neighbor classifiers via cutting plane algorithm." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5581058.

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Pilaszy, Istvan, and Tadeusz Dobrowiecki. "Constructing Large Margin Polytope Classifiers with a Multiclass Classification Algorithm." In 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. IEEE, 2007. http://dx.doi.org/10.1109/idaacs.2007.4488417.

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Kerhet, Aliaksei, Francesco Leonardi, Andrea Boni, Paolo Lombardo, Michele Magno, and Luca Benini. "Distributed video surveillance using hardware-friendly sparse large margin classifiers." In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, 2007. http://dx.doi.org/10.1109/avss.2007.4425291.

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Zhao, Qun, Jose C. Principe, and Dongxin Xu. "From hyperplanes to large-margin classifiers: applications of SAR ATR." In AeroSense '99, edited by Firooz A. Sadjadi. SPIE, 1999. http://dx.doi.org/10.1117/12.359940.

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He, Tingting, Yu Hu, and Qiang Huo. "An Approach to Large Margin Design of Prototype-Based Pattern Classifiers." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366313.

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Kostinger, Martin, Paul Wohlhart, Peter M. Roth, and Horst Bischof. "Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.386.

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Shen, Jianqiang, and Thomas G. Dietterich. "A Family of Large Margin Linear Classifiers and Its Application in Dynamic Environments." In Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.15.

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Reports on the topic "Large margin classifiers"

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Vakharia, Vikram, Shoshana Arad, Yonathan Zohar, Yacob Weinstein, Shamila Yusuff, and Arun Ammayappan. Development of Fish Edible Vaccines on the Yeast and Redmicroalgae Platforms. United States Department of Agriculture, February 2013. http://dx.doi.org/10.32747/2013.7699839.bard.

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Abstract:
Betanodaviruses are causative agents of viral nervous necrosis (VNN), a devastating disease of cultured marine fish worldwide. Betanodavirus (BTN) genome is composed of two single-stranded, positive-sense RNA molecules. The larger genomic segment, RNA1 (3.1 kb), encodes the RNA-dependent RNA polymerase, while the smaller genomic segment, RNA 2 (1.4kb), encodes the coat protein. This structural protein is the host-protective antigen of VNN which assembles to form virus-like particles (VLPs). BTNs are classified into four genotypes, designated red-spotted grouper nervous necrosis virus (RGNNV), barfin flounder nervous necrosis virus (BFNNV), tiger puffer nervous necrosis virus (TPNNV), and striped jack nervous necrosis virus (SJNNV), based on phylogenetic analysis of the coat protein sequences. RGNNV type is quite important as it has a broad host-range, infecting warm-water fish species. At present, there is no commercial vaccine available to prevent VNN in fish. The general goal of this research was to develop oral fish vaccines in yeast and red microalgae (Porphyridium sp.) against the RGNNV genotype. To achieve this, we planned to clone and sequence the coat protein gene of RGNNV, express the coat protein gene of RGNNV in yeast and red microalgae and evaluate the immune response in fish fed with recombinantVLPs antigens produced in yeast and algae. The collaboration between the Israeli group and the US group, having wide experience in red microalgae biochemistry, molecular genetics and large-scale cultivation, and the development of viral vaccines and eukaryotic protein expression systems, respectively, was synergistic to produce a vaccine for fish that would be cost-effective and efficacious against the betanodavirus infection.
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