Machine Learning. Certain Subscription Content may include machine learning, which are taught and trained largely from Customer’s internal data sets. Therefore, the quality of the results and outputs of the machine learning portions of the Subscription Content (such as optimized price proposals and recommended store order quantities, etc.) (“Machine Learning Outputs”) is heavily reliant on the quality of the Customer Data.
Machine Learning. Machine learning is the process of learning patterns from available data to make predictions that generalize to “future unseen” data. It is generally divided into two major types: supervised and unsupervised learning. When labels are available for the dataset, a supervised learning approach is often used to learn how to predict these labels from the features provided. When labels are not available, an “unsupervised” approach is used, where there is no phenotype or outcome to predict, but a supposed underlying structure of the data is being discovered. A fundamental concept in machine learning is data separation and the quest for generalization. Before making any predictions, the data is divided into training and testing sets. The testing set, also known as the “held out” set, is used to test how generalizable the trained model would be if it were to be used on future unseen data. A simple example to illustrate this is polynomial fitting. Suppose we have two synthetically-generated random variables (X1 and X2), which when drawn in a scatter plot (with X1 and X2 being the two axes) have no underlying pattern. Given a polynomial fitting algorithm, it is possible to explain much of the variance of the data with a very high-degree polynomial, given enough training iterations. In other words, without restraint on model complexity, it is possible to explain almost any dataset to an arbitrary level of accuracy. This does not mean, of course, that the model will have any meaning or generalization, and indeed our high degree polynomial is very unlikely to be even close to accurate when it is applied to the testing data. This is known as model “overfitting”. There is a well-known trade-off between model fitting and generalization, and there almost always exists a “sweet spot” where the model fits the training data well enough to have any meaning, but is generalizable enough to allow for utility over future unseen data. Most of the machines learning algorithms require tuning of model “hyperparameters.” In regularized linear models, for example, it is necessary to determine how much to penalize the weights, and in neural networks, it is necessary to determine what network architecture and learning behavior, including the number of nodes per layer (width), the number of layers (depth), the learning rate, the type of non-linearity and the type of optimizer to use. If we were to tune these parameters on the testing set, we would be defeating the purpose of an independent,...
Machine Learning. Random Forest RF trained in PP used R package randomForest, as has been done before.47 The following settings were used: 1000 trees, 30% of the features were randomly selected to compute the best split point, with no limit on the maximum depth of the tree. For scikit-learn the same settings were used except for the multiclass models where the depth of the tree was set to 10 due to memory limitations (>120 GB memory usage). For the multiclass RF, a probability of each class (both active/inactive) was calculated for each entry. The highest probability was chosen as the predicted label and compared to actual experimental label (e.g. for adenosine: A2A_active, A2B_inactive). Machine Learning – Neural Networks Given the novelty of DNN, they are elaborately described in this section below. Neural networks were accelerated on GPUs using nolearn/Lasagne and Theano packages.31,33,34 Feedforward neural networks were constructed using techniques similar to previous studies.16 Models were trained using batch gradient descent with Xxxxxxxx momentum using a batch size of 128.48 For both the learning rate and Nesterov momentum we used an adaptive version: the starting Nesterov momentum was set to 0.8 and 0.999 for the last epoch. The learning rate for the first epoch was set to 0.005 and 0.0001 for the last epoch. The networks consisted of the input layer (e.g. 256 fingerprints) connected to 3 layers of 4000, 2000, 1000 rectified linear units (ReLu) and a linear output layer. The linear output layer consisted of the number of targets modelled (e.g. 1227 for the multi-task network). Because a linear output was used, pChEMBL values were predicted, which were subse- quently converted to classes (pChEMBL > 6.5 = active, pChEMBL ≤ 6.5 = inactive). The target protein features were scaled to zero mean and unit variance. To prevent overfitting of the networks, we used 25% dropout on the hidden layers together with early stop- ping.49 The early stopping validates the loss on an evaluation set (20% of the data) and stops training if the network does not improve on the evaluation set after 200 epochs. The maximum number of iterations was set to 2000 epochs. For the multi-task QSAR models the output layer consists of one output node for each target. The output for a particular compound is going to be sparse, i.e. for most targets there will be no known activity. During training, only targets for which we have data were taken into account when com- puting the error function to update...
Machine Learning. Client agrees and instructs that Heyday may use Conversational Data to develop and improve the capabilities of the Services and Heyday's machine-learning technologies, both during and after the term of the Agreement, provided that (i) such Conversational Data shall be anonymized so that no individual can be specifically identified; and (ii) such Conversational Data shall not be shared with any other client or customer.
Machine Learning. The goal of this task is to implement and test supervised learning algorithms and enable the FEE AI to define finer and more nuanced rules and relationships between entities in the ontology as the dataset grows. This task integrates the machine learning capabilities into the UI. The Recipient shall: • Write an ML Development and Test Plan that describes the development and testing plan for ML subtasks under Task 4. • Build and test machine learning for prediction of missing plant data (Subtask 4a) by implementing supervised learning algorithms which should: ○ Examine the relationships between the data. ○ Use the relationships to predict / estimate values for missing data in the new instances in the ontology. ○ Supplement explicitly specified data estimation procedures. • Build and test machine learning capabilities that enable the AI to derive new rules (Subtask 4b). • Build and test a UI for the machine learning capabilities (Subtask 4c) that should be able to do, but not be limited to, the following: ○ Offer users a predicted value as a suggestion that the user may accept or may rather explicitly specify a value. ○ Report a new rule derived by the AI to the user so that the user can audit the rule for accuracy. • Write ML Test Report describing the methods and results of the machine learning tests in this task. Products: • ML Development and Test Plan • ML Test Report
Machine Learning. Customer acknowledges that a fundamental component of the Moveworks Product is the use of machine learning for the purpose of improving and providing Moveworks’ products and services. Notwithstanding anything to the contrary, Customer agrees that Moveworks is hereby granted the right to use (during and after the term hereof) IT and employee service helpdesk ticket information submitted hereunder to train its algorithms internally through machine learning techniques for such purpose.
Machine Learning the End-User is entitled to access or use the Processing Blocks for the purpose of developing or training machine learning algorithms.
Machine Learning. It is the branch of statistical learning within artificial intelligence which has became known as machine learning. Machine learning pulls together multiple methods of sophisticated statistical processing and is able to perform them on larger and more complex datasets than was previously possible, often with a built in mechanism to improve on its own performance over time (hence learning) (46). Machine learning encompasses multiple Bayesian techniques for pattern recognition and learning, including certain traditional statistical methods such as logistic regression and linear regression (50). In fact, many neural networks include multiple logistic regression expressions. Also included are artificial neural networks, support vector machines (75), K-nearest neighbors (76), random forest plots (77) and decision trees (78).
Machine Learning. Usage Data and Customer Content may be used to develop, train, or enhance artificial intelligence or machine learning models that are part of Provider's products and services, including third-party components of the Product, and Customer authorizes Provider to process its Usage Data and Customer Content for such purposes. However, (a) Usage Data and Customer Content must be aggregated before it can be used for these purposes, and (b) Provider will use commercially reasonable efforts consistent with industry standard technology to de-identify Usage Data and Customer Content before such use. Nothing in this section will reduce or limit Provider's obligations regarding Personal Data that may be contained in Usage Data or Customer Content under Applicable Data Protection Laws. Due to the nature of artificial intelligence and machine learning, information generated by these features may be incorrect or inaccurate. Product features that include artificial intelligence or machine learning models are not human and are not a substitute for human oversight.
Machine Learning. Machine learning algorithms such as Support Vector Machines (SVM) which has been already commonly used in EEG signals, e.g. (Xxxxx and Ubeyli, 2007), Echo State Networks (Xxxxxx, 2001), or Random Forests (Xxxxxxx, 2001) will be also used for classification purposes, in order to automatically distinguish across various states of consciousness. Although these classifiers have been extensively used in other fields, it is still a challenging issue to automatically discriminate across disorders of consciousness (Noirhomme et al., 2015). Moreover, feature and/or decision fusion of the most relevant features for consciousness estimation will be carried out, in order to combine the various features that we will develop throughout the project and to automatically distinguish across various states of consciousness in an optimized way. For instance, Sitt et. al. (Xxxx et al., 2014) used a linear SVM classifier to discriminate between MCS and UWS, and revealed that low-frequency EEG power, EEG complexity, and information exchange when combined a low an automatic classification of a patient’s state of consciousness with an area under curve (AUC) of 78%. Also, in (Xxxxxx et al., 2014) the authors applied SVM classification between MCS and UWS patients, and healthy controls, and revealed that features such as partial coherence, directed transfer function, and generalized partial directed coherence yielded accuracies significantly higher than chance. Simpler classifiers have been also used for automatic recognition of diseases of consciousness. For instance a linear discriminant analysis (LDA) classifier was used in a nested block-wise cross-validation scheme, to discriminate across various diseases of consciousness through complex mental imagery and passive feet movements tasks (Horki et al., 2014). Although various classification approaches have been already used in consciousness research, advanced, more recent classification approaches, such as deep neural networks using autoencoders, echo state networks or random forests still lack attention. Due to the complicated nature of consciousness and to the many different features and their properties related to it, we believe that advanced machine learning approaches that can learn the structure of complex data can reveal additional information about consciousness.
