The three problems that concern us are identifying a natural domain of pattern classification applications of feed forward neural networks, select(cid:173) ing an appropriate feedforward network architecture, and assessing the tradeoff between network complexity, training set size, and statistical reli(cid:173) ability as measured by the probability of incorrect classification. We close with some suggestions, for improving the bounds that come from Vapnik(cid:173) Chervonenkis theory, that can narrow, but not close, the chasm between theory and practice. The goal is to provide rapid, reliable classification of new inputs from a (1) pattern source. Neural networks are appropriate as pattern classifiers when the pattern sources are ones of which we have little understanding, beyond perhaps a nonparametric statistical model, but we have been provided with classified samples of features drawn from each of the pattern categories. Neural networks should be able to provide rapid and reliable computation of complex decision functions.

This article was published as a part of the Data Science Blogathon. The generalization of machine learning models is the ability of a model to classify or forecast new data. When we train a model on a dataset, and the model is provided with new data absent from the trained set, it may perform well. Such a model is generalizable. It doesn't have to act on all data types but with similar domains or datasets.

Ahmedabad is quickly becoming the top choice for recent graduates seeking to launch their careers in the city's IT and software businesses. With so many top-notch IT companies to choose from, it's surely a terrific spot to get started. It is predicted that in the years to come, demand for AI and associated technologies would grow rapidly. Businesses will employ AI to develop better solutions and to develop specialised internal solutions. Naturally, there will be an increase in demand for AI in the future. It makes sense that one of the most in-demand skills in the technological field, which has impacted practically every industry from automotive, manufacturing, and healthcare to cybersecurity, software, and IoT, is AI.

As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. The whole calculation process is turned into what is commonly referred to as a "black box" that is impossible to interpret. These black box models are created directly from the data. And, not even the engineers or data scientists who create the algorithm can understand or explain what exactly is happening inside them or how the AI algorithm arrived at a specific result. Explainability can help developers ensure that the system is working as expected, it might be necessary to meet regulatory standards, or it might be important in allowing those affected by a decision to challenge or change that outcome.

Belief revision studies strategies about how agents revise their belief states when receiving new evidence. Both in classical belief revision and in epistemic revision, a new input is either in the form of a (weighted) propositional formula or a total pre-order (where the total pre-order is considered as a whole). However, in some real-world applications, a new input can be a partial pre-order where each unit that constitutes the partial pre-order is important and should be considered individually. To address this issue, in this paper, we study how a partial pre-order representing the prior epistemic state can be revised by another partial pre-order (the new input) from a different perspective, where the revision is conducted recursively on the individual units of partial pre-orders. We propose different revision operators (rules), dubbed the extension, match, inner and outer revision operators, from different revision points of view. We also analyze several properties for these operators.

Humans learn from past experiences, Machines follow the instructions given by humans but, what if humans can train the machines to learn from the past experiences (data) and can do act much faster, here comes the concept of Machine Learning. Machine learning is the field of study that gives computers the capability to learn without being explicitly programmed. Machine learning algorithms build a mathematical model based on the data, known as training data, in order to make predictions or decisions. Machine learning is not only about learning, but also about understanding and reasoning. Machine Learning is not programmed, it is taught with data.

I recently decided to dive into machine learning, a field I have wanted to understand for a long time but have never had the time to pursue. I've been taking the free (and amazing!) course from Stanford University's Andrew Ng on Coursera. The first two weeks are dedicated to the Linear Gradient algorithm. In this post, I'll provide an overview of how it works and share how I implemented the vectorized version and parts of the non-vectorized version in Golang using the gonum library. Linear regression is a technique used in modeling the linear relationship between an input and its output.

Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.

Programming by example (PBE) systems allow end users to easily create programs by providing a few input-output examples to specify their intended task. The system attempts to generate a program in a domain specific language (DSL) that satisfies the given examples. However, a key challenge faced by existing PBE techniques is to ensure the robustness of the programs that are synthesized from a small number of examples, as these programs often fail when applied to new inputs. This is because there can be many possible programs satisfying a small number of examples, and the PBE system has to somehow rank between these candidates and choose the correct one without any further information from the user. In this work we present a different approach to PBE in which the system avoids making a ranking decision at the synthesis stage, by instead synthesizing a disjunctive program that includes the many top-ranked programs as possible alternatives and selects between these different choices upon execution on a new input. This delayed choice brings the important benefit of comparing the possible outputs produced by the different disjuncts on a given input at execution time. We present a generic framework for synthesizing such disjunctive programs in arbitrary DSLs, and describe two concrete implementations of disjunctive synthesis in the practical domains of data extraction from plain text and HTML documents. We present an evaluation showing the significant increase in robustness achieved with our disjunctive approach, as illustrated by an increase from 59% to 93% of tasks for which correct programs can be learnt from a single example.