Britain’s cabinet considered desperate stratagems as Germany’s grip tightened on continental Europe in late spring 1940. One hinged on tying France and Great Britain indissolubly, which idea Winston Churchill presented to Premier Paul Reynaud. This possibility died as French officialdom decided to seek armistice terms. British contingency plans meanwhile entailed transferring the Royal Family to Canada and waging guerrilla resistance should German armies capture the Isles. British gold and foreign exchange and security reserves, meantime, were shipped to Canada for safety. Churchill’s cabinet debated whether to explore terms with Hitler. Despite doubt and divisions, London rejected capitulation. Churchill insisted that the Royal Navy and Royal Air Force could prevent any cross-Channel assault by the Wehrmacht. As events might warrant, the United States too could be counted upon, Churchill averred. In 1939-1941, well before Congress declared war on the Axis powers, thousands of American volunteers served in British and Canadian land, sea, and air forces. One was poet-pilot John Magee. Virginia Hall served with great distinction in Britain’s clandestine Special Operations Executive.

The US volunteers who fought for the Republic during the Spanish Civil War were of diverse backgrounds. Large numbers came from the laboring class and immigrant families. The Great Depression took a toll on these people with alarming rates of divorce, the abandonment of children to reformatories or foster institutions, and law breaking. To these people, Spain was an extension of US social struggles centered on union organizing, picket line violence, and unemployment councils. White-collar occupations were represented by schoolteachers, office clerks, journalists, and college instructors. A few volunteers traced their lineage to the early Colonial era and sprang from the privileged or professional classes, sporting Ivy League pedigrees. Although all regions of the United States contributed, northeastern urban areas pulled heaviest, with New York City accounting for 18 percent of the inductees. As for commonalities, these stood out: The Americans were relatively young and had scant military experience. Robert Merriman occupied greater status than most Americans in Spain. At the time of his death (April 1938), he held the rank of major and responsibility as chief of staff for the 15th Brigade.

This chapter discusses the frequency of stasis at Thebes. In contrast to existing scholarship, which focuses exclusively on cases that can be diagnosed with (near) certainty, it estimates the total number of staseis that occurred. This approach frees me to think probabilistically and thus factor into my analysis events that probably involved stasis, contextual factors that increased the likelihood of stasis, and – most significantly – the knowledge, acquired in Chapters 5 and 6, that the absence of evidence for stasis cannot be interpreted as evidence of absence except in a tiny minority of the polis-years under consideration. Through comprehensive analysis of the relevant evidence, it argues that Thebes experienced between 17 and 23 staseis during the fifth and fourth centuries; and thus that the Thebans experienced stasis at an average rate of between once every 8 and once every 11 years.

10.1 Support Vector Machines

Support vector machines (SVMs) are supervised machine learning (ML) models used to solve regression and classification problems. However, it is widely used for solving classification problems. The main goal of SVM is to segregate the n-dimensional space into labels or classes by defining a decision boundary or hyperplanes. In this chapter, we shall explore SVM for solving classification problems.

10.1.1 SVM Working Principle

SVM Working Principle | Parteek Bhatia, https://youtu.be/UhzBKrIKPyE

To understand the working principle of the SVM classifier, we will take a standard ML problem where we want a machine to distinguish between a peach and an apple based on their size and color.

Let us suppose the size of the fruit is represented on the X-axis and the color of the fruit is on the Y-axis. The distribution of the dataset of apple and peach is shown in Figure 10.1.

To classify it, we must provide the machine with some sample stock of fruits and label each of the fruits in the stock as an “apple” or “peach”. For example, we have a labeled dataset of some 100 fruits with corresponding labels, i.e., “apple” or “peach”. When this data is fed into a machine, it will analyze these fruits and train itself. Once the training is completed, if some new fruit comes into the stock, the machine will classify whether it is an “apple” or a “peach”.

Most of the traditional ML algorithms would learn by observing the perfect apples and perfect peaches in the stock, i.e., they will train themselves by observing the ideal apples of stock (apples which are very much like apples in terms of their size and color) and the perfect peaches of stock (peaches which are very much like peaches in terms of their size and color). These standard samples are likely to be found in the heart of stock. The heart of the stock is shown in Figure 10.2.