In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap, as wide as possible. New examples are mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces; when data are unlabelled, supervised learning is not possible, an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, map new data to these formed groups.
The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, is one of the most used clustering algorithms in industrial applications. Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes, the goal is to decide which class a new data point will be in. In the case of support-vector machines, a data point is viewed as a p -dimensional vector, we want to know whether we can separate such points with a -dimensional hyperplane; this is called a linear classifier. There are many hyperplanes. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So we choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum-margin classifier.
More formally, a support-vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class, since in general the larger the margin, the lower the generalization error of the classifier. Whereas the original problem may be stated in a finite-dimensional space, it happens that the sets to discriminate are not linearly separable in that space. For this reason, it was proposed that the original finite-dimensional space be mapped into a much higher-dimensional space making the separation easier in that space. To keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors may be computed in terms of the variables in the original space, by defining them in terms of a kernel function k selected to suit the problem.
The hyperplanes in the higher-dimensional space are defined as the set of points whose dot product with a vector in that space is constant, where such a set of vectors is an orthogonal set of vectors that defines a hyperplane. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters α i of images of feature vectors x i that occur in the data base. With this choice of a hyperplane, the points x in the feature space that are mapped into the hyperplane are defined by the relation ∑ i α i k = constant. Note that if k becomes small as y grows further away from x, each term in the sum measures the degree of closeness of the test point x to the corresponding data base point x i. In this way, the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. Note the fact that the set of points x mapped into any hyperplane can be quite convoluted as a result, allowing much more complex discrimination between sets that are not convex at all in the original space.
SVMs can be used to solve various real-world problems: SVMs are helpful in text and hypertext categorization, as their application can reduce the need for labeled training instances in both the standard inductive and transductive settings. Some methods for shallow
I, Me, Mine is an autobiographic work by the English rock musician and former Beatle George Harrison. It was published in 1980 as a hand-bound, limited edition book by Genesis Publications, with a mixture of printed text and multi-colour facsimiles of Harrison's handwritten song lyrics, it was narration by Derek Taylor. The Genesis limited edition sold out soon after publication, it was subsequently published in hardback and paperback in black ink by W H Allen in London and by Simon & Schuster in New York; the project marked a departure for Genesis Publications, which had focused on facsimile editions of historical nautical journals, including The Log of H. M. S. Bounty 1787–1789. Brian Roylance, who founded the company in 1974, said of Harrison's memoir: "I saw the song lyrics as important documents – as important as all the other things I was publishing." Genesis subsequently became a leading publisher of rock music-related illustrated books, including further titles by Harrison and Taylor, as well as books about the Beatles, the Rolling Stones, David Bowie and Pink Floyd, among others.
I, Me, Mine was released a few months before John Lennon's murder in December 1980. Lennon had taken offence at Harrison's book, telling interviewer David Sheff: "I was hurt by it... By glaring omission in the book, my influence on his life is zilch and nil... I'm not in the book." Harrison, in fact, does mention Lennon several times. In December 1987, Harrison was asked about Lennon's comments by Selina Scott on the television show West 57th Street, he told her: " was annoyed'cause I didn't say that he'd written one line of this song'Taxman'. But I didn't say how I wrote two lines of'Come Together' or three lines of'Eleanor Rigby', you know? I wasn't getting into any of that. I think, in the balance, I would have had more things to be niggled with him about than he would have had with me."I, Me, Mine was re-published with a new foreword by Harrison's widow, Olivia, in 2002. A third version of the book, now containing "59 additional handwritten lyrics and unpublished photographs not found in the original printing", was released in February 2017 to mark what would have been Harrison's 74th birthday
Leigh Hennessy is a world trampoline champion who runs the trampoline Website Trampoline Pundit. In addition to being the Trampoline Pundit, she enjoys a career as an actress and stunt performer in the film and television industries. In addition she has co-authored two recent books with her husband, Mark Robson): The Day of the Cajundome Mega-Shelter and Don't Get Stuck on Stupid. Leigh's father, Jeff Hennessy, a retired associate professor at the University of Louisiana at Lafayette was an expert in the sport of trampolining and was her first trampoline coach. According to Guinness World Records, she holds the record for winning the most US national championships in trampoline for women, she competed for the United States in several world championships in individual trampoline, synchronized trampoline, double mini-trampoline, winning two world titles. While Leigh was one of the best female trampolinists in United States history, she was a successful trampoline coach, she founded a trampoline academy in Lafayette and served as a coach for the United States national team.
After earning a master's degree in communication at the University of Louisiana at Lafayette and working for Congressman Jimmy Hayes in Washington, D. C. Leigh's career shifted to stunt work in Hollywood, she has worked as Demi Moore's stunt double in GI Jane and many other films, television shows and commercials. In 2006, Leigh starred in the opening scenes of The Guardian, a film with Kevin Costner, in which she played the role of the drowning wife, she was nominated for a Taurus World Stunt Award in 2007 for her work in this movie. More she transitioned into stunt coordinating and coaching stunt performers. In August 2007, Hennessy was inducted into the USA Gymnastics Hall of Fame in recognition of her trampoline achievements, her father was a member of the Hall of Fame, they are the first father and daughter to be inducted. World Acrobatics Society Legends Hall of Fame Southern AAU Athlete of the Year Lifetime achievement award from the International Trampoline Federation Taurus World Stunt Award nomination Official site Leigh Hennessy on IMDb USA Gymnastics Hall of Fame
Muzzy in Gondoland is an animated film first created by the BBC in 1986 as a way of teaching English as a second language. The English version of Muzzy features the voices of Willie Rushton, Miriam Margolyes, Susan Sheridan, Derek Griffiths, Jack May and Benjamin Whitrow. DMP Organization acquired the rights to Muzzy and translated it into other languages, it is unknown, who plays whom in the various dubbed versions of the film. A sequel, Muzzy Comes Back was released in 1989. Digital Education has developed a new version of the course, released in 2013. Muzzy, a large bear-like extraterrestrial, arrives from outer space to visit Gondoland, ruled over by King Nigel and Queen Ezra, their daughter is Princess Sylvia, Bob is their gardener and Corvax is an evil scientist who works for the King. Bob and Sylvia decide to elope. However, who loves Sylvia, sees what is happening, informs the King and Queen. Angered, the King catches them. Sylvia is taken back to the palace. Bob shares a cell with Muzzy. Realizing Muzzy's unusual diet, Bob encourages Muzzy to eat the bars of the prison cell and they escape.
Meanwhile, back at the palace, Corvax tries flirting with Sylvia, but she angrily protests that she loves Bob. Crestfallen, Corvax uses his computer to clone her, but the duplicate hates him just as much as the original. Furious, Corvax bangs the computer frantically, causing it to explode and produce six more duplicates who roam around the palace. Bob returns to the palace with Muzzy, she tells them to wait for her to bring them food there. Back in the computer lab, Corvax attempts to recall the six duplicates of Sylvia, but the computer malfunctions and begins to infinitely produce Sylvia clones. Before long, hundreds of duplicates are swarming the palace. Down on the ground floor, Nigel notices that the Sylvia duplicates are coming from Corvax's room, decides to investigate. Seeing that Corvax cannot stop the computer, Nigel decides to fix it himself, he pulls a plug attached to the computer, which stops the process, but he is sucked up inside the computer himself instead. Not knowing how to save Nigel, Corvax decides to escape by helicopter.
Seeing him do so, Bob chases after him. Sylvia and Muzzy enter the computer lab, Muzzy is able to release the King from inside the machine. Bob returns with Corvax, is exonerated while Corvax is taken away by the King's troops. Bob and Sylvia are reunited, Nigel gives them his blessing. Afterwards, Muzzy manages to send all of the Sylvia duplicates back into the computer. Bob and Sylvia get married, Muzzy leaves Gondoland in his spaceship. Jack May as Muzzy Willie Rushton as King Nigel and Additional Voices Miriam Margolyes as Queen Ezra and Norman's wife human Susan Sheridan as Princess Sylvia and Corvax's cat Derek Griffiths as Bob and Additional Voices Benjamin Whitrow as Norman DMP Organization, worldwide distributor of the Muzzy courses, has licensed the development of the New Muzzy to Digital Education SA. There are various distributors of the course around the world; the original system was available in the following languages: French, Italian, German and Esperanto. The program was well known for its long-running television commercial, which received heavy airplay on Nickelodeon.
As of 2018, it is now available in the following languages: English, French, Italian, Mandarin Chinese and Spanish. Muzzy in Gondoland on IMDb
A gendarmerie or gendarmery is a military component with jurisdiction in civil law enforcement. The term gendarme is derived from the medieval French expression gens d'armes, which translates to "armed people". In France and some Francophone nations, the gendarmerie is a branch of the armed forces responsible for internal security in parts of the territory with additional duties as a military police for the armed forces; this concept was introduced to several other Western European countries during the Napoleonic conquests. In the mid-twentieth century, a number of former French mandates or colonial possessions such as Lebanon and the Republic of the Congo adopted a gendarmerie after independence; the growth and expansion of gendarmerie units worldwide has been linked to an increasing reluctance by some governments to use military units entrusted with external defense for combating internal threats. A somewhat related phenomenon has been the formation of paramilitary units which fall under the authority of civilian police agencies.
Since these are not military forces, they are not considered gendarmerie. Some of the more prominent modern gendarmerie organizations include the French National Gendarmerie, Spanish Civil Guard, Italian Carabinieri, Portuguese National Republican Guard and the Turkish Gendarmerie; the word gendarme is a singular extracted from Old French gens d'armes. During the Late Medieval to the Early Modern period, the term referred to a armoured cavalryman of noble birth serving in the French army; the word gained policing connotations only during the French Revolution when the Maréchaussée of the Ancien Régime, attached to the Gendarmerie before, by the Monarchy, was renamed, "Gendarmerie". The spelling in English was gendarmery, but now the French spelling gendarmerie is more common; the Oxford English Dictionary uses gendarmery as the principal spelling. These forces are titled "gendarmerie", but gendarmeries may bear other titles, for instance the Carabinieri in Italy, the Guarda Nacional Republicana in Portugal, the Guardia Civil in Spain, the Royal Marechaussee in the Netherlands or Internal Troops/National Guard in Ukraine and Russia.
As a result of their duties within the civilian population, gendarmeries are sometimes described as "paramilitary" rather than "military" forces although this description corresponds to their official status and capabilities. Gendarmes are rarely deployed in military situations, except in humanitarian deployments abroad. A gendarmerie may come under the authority of a ministry of defence, a ministry of the interior, or both at once. There is some coordination between a ministry of defence and a ministry of the interior over the use of gendarmes. A few forces which are no longer considered military retain the title "gendarmerie" for reasons of tradition. For instance, the French language title of the Royal Canadian Mounted Police is Gendarmerie royale du Canada because this force traditionally had some military-style functions and has retained its status as a regiment of dragoons; the Argentine Gendarmerie is a military force in terms of training and public perception, was involved in combat in the Falklands War, however it is classified as a "security force" not an "armed force", to exercise jurisdiction over the civilian population under Argentine law.
Since different countries may make different use of institutional terms such as "gendarmerie", there are cases in which the term may become confusing. For instance, in the French-speaking Cantons of Switzerland the "gendarmeries" are the uniformed civil police. In Chile, the word "gendarmerie" refers for historic reasons to the prison service, while the actual gendarmerie force is called the "carabineros". In some cases, a police service's military links are ambiguous and it can be unclear whether a force should be defined as a gendarmerie; some historical military units, such as South-West Africa's Koevoet, were only defined as police for political reasons. Services such as the Italian Guardia di Finanza would be defined as gendarmeries since the service is of an ambiguous military status and does not have general policing duties amongst the civilian population. In Russia, the modern National Guard are military units with quasi-police duties but different bodies within the Tsarist Special Corps of Gendarmes performed a variety of functions as an armed rural constabulary, urban riot control units, frontier guards, intelligence agents and political police.
Prior to the creation of the Irish Free State in 1922, British rule was based on the Royal Irish Constabulary—a drilled and armed force located in rural "barracks", a gendarmerie in all but in name. In 2014 the Mexican Federal Police, a armed force which has many attributes of a gendarmerie, created a new seventh branch of service called the National Gendarmerie Division; the new force would number 5,000 personnel and was created with the assistance of the French gendarmerie. In comparison to civilian police forces, gendarmeries may provide a more disciplined force whose military capabilities (e.g. armored group in France with armored personnel c
Portland Arch Nature Preserve is a 435-acre nature preserve near the Wabash River in Fountain County, Indiana, USA, is a National Natural Landmark. The preserve encompasses the wooded valleys and rocky cliffs around the lowest section of Bear Creek, which flows northwest toward the Wabash River, its name comes from the nearby town of Fountain, named Portland, from a natural sandstone bridge carved by a small tributary of Bear Creek. Portland Arch Nature Preserve is managed by the Indiana Department of Natural Resources. Portland Arch is one of only a few natural arches in Indiana; the arch was created through the Mansfield Sandstone by Bear Creek, undercutting the bluff on both sides. The sandstone was strong enough; the Mansfield Sandstone is from some 230 million years ago. The cross stratification of the layers were formed in s shallow bottomed lake where it drops into deeper water; the slope reflects the change from the shallows into deeper water. Are like this were used as rock shelters by early pioneers.