Machine Learning. The parties acknowledge that machine learning (‘ML’) is a subset of AI that enables machines to develop algorithms, including via deep learning (as defined below), based on statistical inferences drawn from patterns in submitted training data, including, but not limited to, diffusion models and large language models, for the purpose of performing tasks. Such tasks include, but are not limited to, predicting human behaviors, disseminating information and generating content.
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. 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. 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. Hands-On for Developers and Technical Professionals. Xxxx Xxxxx & Sons. Xxxxxx, X. X., Xxxxxx, K., Xxxxx, J., XxXxxxxx, T., & Xxxxxx, X. X. (2016). Implementation of Web-Based Autism Screening in an Urban Clinic. Clinical Pediatrics, 55(10), 927–934. Xxxxxxxxxxx, X. X., Xxxx, J., Van Naarden Xxxxx, K., Bilder, D., Xxxxxxx, J., Xxxxxxxxxxx, X. X., … Centers for Disease Control and Prevention (CDC). (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. Morbidity and Mortality Weekly Report. Surveillance Summaries , 65(3), 1–23. Xxxxxxxx, X. X., Akshoomoff, N., & Xxxxxxx, X. X. (2013). Diagnosis of autism spectrum disorders in 2-year-olds: a study of community practice. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54(2), 178–185. Xxxxxxx, A. M., Halladay, A. K., Shih, A., Xxxxx, X. X., & Xxxxxx, G. (2014). Approaches to enhancing the early detection of autism spectrum disorders: a systematic review of the literature. Journal of the American Academy of Child and Adolescent Psychiatry, 53(2), 141–152. Xxxx Xxxxx, Xxxx X. Xxxx, and Xxx X. Xxxxxx. (2016). Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques.” Data Mining: Practical Machine Learning Tools and Techniques. Xxxx Xxxxxxx. Retrieved from xxxx://xxx.xx.xxxxxxx.xx.xx/ml/weka/Witten_et_al_2016_appendix.pdf Falkmer, T., Xxxxxxxx, K., Falkmer, M., & Horlin, C. (2013). Diagnostic procedures in autism spectrum disorders: a systematic literature review. European Child & Adolescent Psychiatry, 22(6), 329–340. Xxxxxx-Xxxxxx, E., Xxxxxx, J., & Peacock, G. (2016). Whittling Down the Wait Time: Exploring Models to Minimize the Delay from Initial Concern to Diagnosis and Treatment of Autism Spectrum Disorder. Pediatric Clinics of North America, 63(5), 851–859. Xxxxxxxxxx, X. X., Bai, R., & Xxxxxxx, A. M. (2013). Screening children for autism in an urban clinic using an electronic M-CHAT. Clinical Pediatrics, 52(1), 35–41. How Is Autism Diagnosed? (n.d.). Retrieved February 8, 2017, from xxxxx://xxx.xxxxxxxxxxxx.xxx/what-autism/diagnosis Xxxxxxx, X. X., & Xxxxxxxxxx, X. X. (2004). Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health, 4, 29. Xxxxx, M. (2017). CDC: Autism rates unchanged at 1 in 68 children. AAP News. Retrieved from xxxx://xxx.xxxxxxxxxxxxxxx.xxx/news/2016/03/3...
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. 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.
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. Notwithstanding anything to the contrary, during the Term, Company shall have the right to collect and analyze data and other information relating to the provision, use and performance of various aspects of the Service and related systems and technologies (including, without limitation: Customer Data and data derived therefrom; Customer’s inputs, selections, actions, training and interactions in relation to the Service and Software (the “Collected Data”); and machine learning models, learned elements, biases, attributes, attribute transformations, weights and other data or works derived from Customer Data or Collected Data (the “Learned Elements”). Company shall be the exclusive owner of all intellectual property rights in such Collected Data and Learned Elements and will be free to use Collected Data and Learned Elements to improve and enhance the Service and for other development, diagnostic and corrective purposes in connection with the Service and other Company offerings, provided that the Collected Data and Learned Elements do not contain any Confidential Information of Customer or identify Customer or any Personal Information.
Machine Learning. Random Forest Machine Learning – Neural Networks
