Byzantine EmpireThe Byzantine Empire, also referred to as the Eastern Roman Empire, was the continuation of the Roman Empire primarily in its eastern provinces during Late Antiquity and the Middle Ages, when its capital city was Constantinople. It survived the fall of the Western Roman Empire in the 5th century AD and continued to exist until the fall of Constantinople to the Ottoman Empire in 1453. During most of its existence, the empire remained the most powerful economic, cultural, and military force in the Mediterranean world.
Byzantine armyThe Byzantine army was the primary military body of the Byzantine armed forces, serving alongside the Byzantine navy. A direct continuation of the Eastern Roman army, shaping and developing itself on the legacy of the late Hellenistic armies, it maintained a similar level of discipline, strategic prowess and organization. It was among the most effective armies of western Eurasia for much of the Middle Ages. Over time the cavalry arm became more prominent in the Byzantine army as the legion system disappeared in the early 7th century.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Byzantine artByzantine art comprises the body of Christian Greek artistic products of the Eastern Roman Empire, as well as the nations and states that inherited culturally from the empire. Though the empire itself emerged from the decline of Rome and lasted until the Fall of Constantinople in 1453, the start date of the Byzantine period is rather clearer in art history than in political history, if still imprecise. Many Eastern Orthodox states in Eastern Europe, as well as to some degree the Islamic states of the eastern Mediterranean, preserved many aspects of the empire's culture and art for centuries afterward.
Byzantine architectureByzantine architecture is the architecture of the Byzantine Empire, or Eastern Roman Empire. The Byzantine era is usually dated from 330 AD, when Constantine the Great established a new Roman capital in Byzantium, which became Constantinople, until the fall of the Byzantine Empire in 1453. However, there was initially no hard line between the Byzantine and Roman Empires, and early Byzantine architecture is stylistically and structurally distinguishable from earlier Roman architecture.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Byzantine Empire under the Komnenos dynastyThe Byzantine Empire was ruled by emperors of the Komnenos dynasty for a period of 104 years, from 1081 to about 1185. The Komnenian (also spelled Comnenian) period comprises the reigns of five emperors, Alexios I, John II, Manuel I, Alexios II and Andronikos I. It was a period of sustained, though ultimately incomplete, restoration of the military, territorial, economic and political position of the Byzantine Empire.
Adversarial machine learningAdversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. To understand, note that most machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Roman–Persian WarsThe Roman–Persian Wars, also known as the Roman–Iranian Wars, were a series of conflicts between states of the Greco-Roman world and two successive Iranian empires: the Parthian and the Sasanian. Battles between the Parthian Empire and the Roman Republic began in 54 BC; wars began under the late Republic, and continued through the Roman (later Eastern Roman (Byzantine)) and Sasanian empires. A plethora of vassal kingdoms and allied nomadic nations in the form of buffer states and proxies also played a role.
Neural architecture searchNeural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par or outperform hand-designed architectures. Methods for NAS can be categorized according to the search space, search strategy and performance estimation strategy used: The search space defines the type(s) of ANN that can be designed and optimized. The search strategy defines the approach used to explore the search space.
Byzantine silkByzantine silk is silk woven in the Byzantine Empire (Byzantium) from about the fourth century until the Fall of Constantinople in 1453. The Byzantine capital of Constantinople was the first significant silk-weaving center in Europe. Silk was one of the most important commodities in the Byzantine economy, used by the state both as a means of payment and of diplomacy. Raw silk was bought from China and made up into fine fabrics that commanded high prices throughout the world.
Byzantine PapacyThe Byzantine Papacy was a period of Byzantine domination of the Roman papacy from 537 to 752, when popes required the approval of the Byzantine Emperor for episcopal consecration, and many popes were chosen from the apocrisiarii (liaisons from the pope to the emperor) or the inhabitants of Byzantine-ruled Greece, Syria, or Sicily. Justinian I reconquered the Italian peninsula in the Gothic War (535–554) and appointed the next three popes, a practice that would be continued by his successors and later be delegated to the Exarchate of Ravenna.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Loss functions for classificationIn machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). Given as the space of all possible inputs (usually ), and as the set of labels (possible outputs), a typical goal of classification algorithms is to find a function which best predicts a label for a given input .
Group method of data handlingGroup method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition. GMDH algorithms are characterized by inductive procedure that performs sorting-out of gradually complicated polynomial models and selecting the best solution by means of the external criterion.
Sufficient statisticIn statistics, a statistic is sufficient with respect to a statistical model and its associated unknown parameter if "no other statistic that can be calculated from the same sample provides any additional information as to the value of the parameter". In particular, a statistic is sufficient for a family of probability distributions if the sample from which it is calculated gives no additional information than the statistic, as to which of those probability distributions is the sampling distribution.
History of BulgariaThe history of Bulgaria can be traced from the first settlements on the lands of modern Bulgaria to its formation as a nation-state, and includes the history of the Bulgarian people and their origin. The earliest evidence of hominid occupation discovered in what is today Bulgaria date from at least 1.4 million years ago. Around 5000 BC, a sophisticated civilization already existed which produced some of the first pottery, jewellery and golden artifacts in the world. After 3000 BC, the Thracians appeared on the Balkan Peninsula.
Sample size determinationSample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power.
