Mastering Algorithms With C Useful Techniques F... VERIFIED
Performance Comparison: By solving recurrences, it is possible to compare the running times of different algorithms, which can be useful in selecting the best algorithm for a particular use case.
Mastering Algorithms with C Useful Techniques f...
The master theorem always yields asymptotically tight bounds to recurrences from divide and conquer algorithms that partition an input into smaller subproblems of equal sizes, solve the subproblems recursively, and then combine the subproblem solutions to give a solution to the original problem. The time for such an algorithm can be expressed by adding the work that they perform at the top level of their recursion (to divide the problems into subproblems and then combine the subproblem solutions) together with the time made in the recursive calls of the algorithm. If T ( n ) \displaystyle T(n) denotes the total time for the algorithm on an input of size n \displaystyle n , and f ( n ) \displaystyle f(n) denotes the amount of time taken at the top level of the recurrence then the time can be expressed by a recurrence relation that takes the form:
Machine Learning algorithms are mainly divided into four categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning , as shown in Fig. 2. In the following, we briefly discuss each type of learning technique with the scope of their applicability to solve real-world problems.
Thus, to build effective models in various application areas different types of machine learning techniques can play a significant role according to their learning capabilities, depending on the nature of the data discussed earlier, and the target outcome. In Table 1, we summarize various types of machine learning techniques with examples. In the following, we provide a comprehensive view of machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.
Rule-based classification: The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction. Several classification algorithms such as Zero-R , One-R , decision trees [87, 88], DTNB , Ripple Down Rule learner (RIDOR) , Repeated Incremental Pruning to Produce Error Reduction (RIPPER)  exist with the ability of rule generation. The decision tree is one of the most common rule-based classification algorithms among these techniques because it has several advantages, such as being easier to interpret; the ability to handle high-dimensional data; simplicity and speed; good accuracy; and the capability to produce rules for human clear and understandable classification  . The decision tree-based rules also provide significant accuracy in a prediction model for unseen test cases . Since the rules are easily interpretable, these rule-based classifiers are often used to produce descriptive models that can describe a system including the entities and their relationships.
Regression analysis includes several methods of machine learning that allow to predict a continuous (y) result variable based on the value of one or more (x) predictor variables . The most significant distinction between classification and regression is that classification predicts distinct class labels, while regression facilitates the prediction of a continuous quantity. Figure 6 shows an example of how classification is different with regression models. Some overlaps are often found between the two types of machine learning algorithms. Regression models are now widely used in a variety of fields, including financial forecasting or prediction, cost estimation, trend analysis, marketing, time series estimation, drug response modeling, and many more. Some of the familiar types of regression algorithms are linear, polynomial, lasso and ridge regression, etc., which are explained briefly in the following.
Density-based methods: To identify distinct groups or clusters, it uses the concept that a cluster in the data space is a contiguous region of high point density isolated from other such clusters by contiguous regions of low point density. Points that are not part of a cluster are considered as noise. The typical clustering algorithms based on density are DBSCAN , OPTICS  etc. The density-based methods typically struggle with clusters of similar density and high dimensionality data.
Grid-based methods: To deal with massive datasets, grid-based clustering is especially suitable. To obtain clusters, the principle is first to summarize the dataset with a grid representation and then to combine grid cells. STING , CLIQUE , etc. are the standard algorithms of grid-based clustering.
Many clustering algorithms have been proposed with the ability to grouping data in machine learning and data science literature [41, 125]. In the following, we summarize the popular methods that are used widely in various application areas.
Monte Carlo methods: Monte Carlo techniques, or Monte Carlo experiments, are a wide category of computational algorithms that rely on repeated random sampling to obtain numerical results . The underlying concept is to use randomness to solve problems that are deterministic in principle. Optimization, numerical integration, and making drawings from the probability distribution are the three problem classes where Monte Carlo techniques are most commonly used.
Overall, based on the learning techniques discussed above, we can conclude that various types of machine learning techniques, such as classification analysis, regression, data clustering, feature selection and extraction, and dimensionality reduction, association rule learning, reinforcement learning, or deep learning techniques, can play a significant role for various purposes according to their capabilities. In the following section, we discuss several application areas based on machine learning algorithms.
Cybersecurity and threat intelligence: Cybersecurity is one of the most essential areas of Industry 4.0. , which is typically the practice of protecting networks, systems, hardware, and data from digital attacks . Machine learning has become a crucial cybersecurity technology that constantly learns by analyzing data to identify patterns, better detect malware in encrypted traffic, find insider threats, predict where bad neighborhoods are online, keep people safe while browsing, or secure data in the cloud by uncovering suspicious activity. For instance, clustering techniques can be used to identify cyber-anomalies, policy violations, etc. To detect various types of cyber-attacks or intrusions machine learning classification models by taking into account the impact of security features are useful . Various deep learning-based security models can also be used on the large scale of security datasets [96, 129]. Moreover, security policy rules generated by association rule learning techniques can play a significant role to build a rule-based security system . Thus, we can say that various learning techniques discussed in Sect. Machine Learning Tasks and Algorithms, can enable cybersecurity professionals to be more proactive inefficiently preventing threats and cyber-attacks.
In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications. According to our goal, we have briefly discussed how various types of machine learning methods can be used for making solutions to various real-world issues. A successful machine learning model depends on both the data and the performance of the learning algorithms. The sophisticated learning algorithms then need to be trained through the collected real-world data and knowledge related to the target application before the system can assist with intelligent decision-making. We also discussed several popular application areas based on machine learning techniques to highlight their applicability in various real-world issues. Finally, we have summarized and discussed the challenges faced and the potential research opportunities and future directions in the area. Therefore, the challenges that are identified create promising research opportunities in the field which must be addressed with effective solutions in various application areas. Overall, we believe that our study on machine learning-based solutions opens up a promising direction and can be used as a reference guide for potential research and applications for both academia and industry professionals as well as for decision-makers, from a technical point of view.
A probabilistic neural network that accounts foruncertainty in weights and outputs. A standard neural networkregression model typically predicts a scalar value;for example, a model predicts a house priceof 853,000. In contrast, a Bayesian neural network predicts a distribution ofvalues; for example, a model predicts a house price of 853,000 with a standarddeviation of 67,200. A Bayesian neural network relies onBayes' Theoremto calculate uncertainties in weights and predictions. A Bayesian neuralnetwork can be useful when it is important to quantify uncertainty, such as inmodels related to pharmaceuticals. Bayesian neural networks can also helpprevent overfitting.
Obtaining an understanding of data by considering samples, measurement,and visualization. Data analysis can be particularly useful when adataset is first received, before one builds the first model.It is also crucial in understanding experiments and debugging problems withthe system.
For example, you might determine that temperature might be a usefulfeature. Then, you might experiment with bucketingto optimize what the model can learn from different temperature ranges.
A family of algorithms that learn an optimal policy, whose goalis to maximize return when interacting withan environment.For example, the ultimate reward of most games is victory.Reinforcement learning systems can become expert at playing complexgames by evaluating sequences of previous game moves that ultimatelyled to wins and sequences that ultimately led to losses. 041b061a72