Objective Functions, Deep Learning and Random Forests
نویسنده
چکیده
Introduction: Science A computer scientist seems an odd choice to speak either about science in the forest, or science in the past. Computer science is more often located in cities and offices than in forests, and is concerned with the challenges of the future rather than the past. ‘Science’ appears to be a point of enquiry shared with this symposium, but even this word is open to debate. It is often observed that a discipline including the word ‘science’ in its name introduces doubt as to why the claim is necessary. While Cambridge has long admired natural philosophy and the ‘natural sciences’, computer science fails to qualify as one of them. We computer scientists do not study nature, but only what we make ourselves. A computer scientist is perhaps more akin to a novelist, sculptor or carpenter than to an astronomer or entomologist. This may be why computer scientists are unusually sensitive to the question of whether their work is objective.
منابع مشابه
Forward Thinking: Building Deep Random Forests
The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neuron...
متن کاملPerceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks
Our objective is to build machine learning based models that predict audiovisual quality directly from a set of correlated parameters that are extracted from a target quality dataset. We have used the bitstream version of the INRS audiovisual quality dataset that reflects contemporary realtime configurations for video frame rate, video quantization, noise reduction parameters and network packet...
متن کاملAutomated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods
OBJECTIVE To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. METHODS Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound imag...
متن کاملResults from a Semi-Supervised Feature Learning Competition
We present results from a recent large-scale semi-supervised feature learning competition, which attracted twenty-nine teams and 238 total submissions. The learning task was drawn from a real world task in malicious url classification. This was a large scale binary classification task, with a sparse feature space of one million features, and training data sets of 50,000 labeled examples and one...
متن کاملRandom Forests Can Hash
Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spectacular results in classification, can also be extended to large-scale retri...
متن کامل