We attempt to understand visual classification in humans using both psy- chophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classi- fied the faces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and shape representation of the faces. The classification perfor- mance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender es- timated by the subjects.

We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vec- tor machines (SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classifiers) using human clas- sification data. Because we combine a linear preprocessor with linear classifiers, the entire system acts as a linear classifier, allowing us to visu- alise the decision-image corresponding to the normal vector of the separ- ating hyperplanes (SH) of each classifier. We predict that the female-to- maleness transition along the normal vector for classifiers closely mim- icking human classification (SVM and RVM [1]) should be faster than the transition along any other direction. A psychophysical discrimina- tion experiment using the decision images as stimuli is consistent with this prediction.

The current research work is being developed as a training and evaluation object. the performance of a predictive model to apply it to the imports of vegetable products into Peru using artificial intelligence algorithms, specifying for this study the Machine Learning models: LSTM and PROPHET. The forecast is made with data from the monthly record of imports of vegetable products(in kilograms) from Peru, collected from the years 2021 to 2022. As part of applying the training methodology for automatic learning algorithms, the exploration and construction of an appropriate dataset according to the parameters of a Time Series. Subsequently, the model with better performance will be selected, evaluating the precision of the predicted values so that they account for sufficient reliability to consider it a useful resource in the forecast of imports in Peru.

Intelligent recommender systems rank among the most fascinating use cases for machine learning. Such systems are used extensively by many of the major tech companies to personalize the selection of user content shown... READ MORE

Typically, the most distinctive feature is predictions by training an event that is likely to happen in the future with the available data sets. Objectives can of course change according to sectoral expectations, but we can emphasize what is common. Facilitating the handling of the focal point separately from the general in most cases makes it easier to understand the whole picture. Because a suitable estimate that will make the composition valid can be constructed this way. An example would be to show the curve of birth-death rates by years in a single graph.

Craniosynostosis is the premature fusion of 1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features.

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling. In this article, we demonstrate how to use this package to do hyperparameter search for a classification problem with Scikit-learn. Below are code fragments showing how to integrate the package with Scikit-learn. We begin by specifying if the problem is one of minimization or maximization.

The seed for this article was planted when Anant was struck by a headline on his Twitter feed: "You don't need ML/AI. He had observed something similar in working through data and analytics requirements for Google Cloud's Apigee team -- not that machine learning (ML) or artificial intelligence (AI) is not needed, but that good database queries can frequently accomplish the job, and that when AI is legitimately needed, its role is often to improve the database design and operations, not to replace them. The two of us got the chance to compile our thinking a bit more as Anant was preparing for a talk at VLDB 2018, a premier database conference. The slides of his talk are here. In this post, we elaborate on some of our observations on the topic. As a leading API management platform, Apigee processes hundreds of billions of API calls every year.

In this special guest feature, Neil Cohen, Vice President at Edge Intelligence, examines the question: where should businesses develop and execute machine learning? This article explores the pros and cons of in the cloud versus at the edge. Neil brings more than 15 years of combined marketing and product management experience to his role as VP of Product Management & Marketing for Edge Intelligence. Previously, he was VP of Global Marketing at Akamai Technologies, where he ran worldwide marketing for a $1.3 billion cybersecurity and web performance business. He was also VP of Product Marketing at Akamai where he helped the organization double revenue and repeatedly launched new products and helped grow them into businesses exceeding hundreds of millions of dollars.

Machine learning, which is just an advanced form of artificial intelligence (AI), is changing how the world operate. There are more use cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). An algorithm is just a procedure for solving a problem, based on conductiong a sequence of specified actions. Machine learning is just a collection of algorithms that learn from data in different ways. These algorithms identify repeatable and persistant patterns in data.