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Yes this is done routinely. For example this is how the YOLO object detection and classifier system works, to give a real-world for example. In YOLO, the "non-object" classification is "background" i.e. any image segment that doesn't contain one of the types of object we are interested in. In general, you can add an "other" class to any classifier, provided ...


3

This seems to fall broadly into the regime of a classification problem as you want to classify an outgoing communication as "contains proprietary information" or "does not contain proprietary information". As such, any classification approach could be applied. Neural Networks certainly seem like a valid approach, but you might also get good mileage out of ...


3

One incredibly important difference between humans and NNs is that the human brain is the result of billions of years of evolution whereas NNs were partially inspired by looking at the result and thinking "... we could do that" (utmost respect for Hubel and Wiesel). Human brains (and in fact anything biological really) have an embedded structure to them ...


3

I suppose the most common part where it will be used is in the initialization of weights before training; the best ways currently known to do that involve randomness. If you use Dropout during training (randomly setting some activation levels to zero to combat overfitting), that also involves randomness, so your seed could also have influence there. Dropout ...


3

I will try to answer all three questions to the best of my ability. Is this basically true? Quick googling just brought me to a lot of papers trying to fit decision trees into incremental learning. The problem with decision trees in an online learning setting is that the model should be able to update when experiences (i.e. state, action, reward, new state)...


3

Depends on what does 1 represent in your task. If you are trying to predict household prices and 1 represents \$1, I think the average validation loss is good. If 1 represents \$10000 in this case, probably something is not right. But remember that there are 2 parts contributing to the overall loss. The mse loss and the l2 penalty loss. (Also remember that ...


3

The validation loss settles exactly at an error of one. Probably means there's something off with either the kind of data validation set has or with something in the training. An exact validation loss of one almost definitely means there's something off. I'd recommend before doing anything thoroughly go through your data or see if there's anything to debug ...


2

Adding to SmallChess's answer , Larger trees(with many nodes) are too adapted to the training set, as a small change in the input train data might cause the trees to change very much and hence change the estimate value too much.This is mainly due to the hierarchical structures of trees(because a change in a higher node may cause all lower nodes to change). ...


2

The bigger your tree is the more overfitting your model is. In machine learning, we always prefer a simpler model unless there is good reason to go for complication.


2

For your first question,take a look at using a skip grab model to find what the abbreviated text is. The skip gram model turns a word into a vector which allows it to be processed by other machine learning algorithms. Or , alternatively you can do some really cool addition and subtraction problems with the resulting vectors. With the skip gram model in your ...


2

There is no transformation between epochs. One full iteration over the training set is considered an epoch. Lets assume: We're considering a gradient descent in a space without local optima. This means that if you'd plot the errors we calculate below, this plot has but one lowest point. The perceptron is intended to model a linearely seperable function. So,...


2

I wouldn't focus only on "deep learning" unless you have some specific reason for doing so. There may be other techniques which could be as effective, or more effective. One approach I've seen used for something similar was Inductive Logic Programming. For one example of using ILP to reason about elements of biochemistry, see this paper That's not ...


2

The The Oxford Companion to Chess has entries on only 700 named openings, and lists another 1327 opening variations in the index, and I wouldn't be surprised if someone out there had them all memorized. For an algorithm, however, storing that number of openings is trivial, and Chess algorithms traditionally made use of high-quality "game books" which are ...


2

If you can remember everything and there's no randomisation in your outcome like chess, there is absolutely no reason not to do that. Anybody who can remember all the possible board configurations in chess, by definition plays perfect chess. A perfect player would never lose. Unfortunately, most practical problems can't be solved by brute-force, and that ...


2

Any AI algorithm depends on the environment, and available actuators and sensors. In our case, the environment is a road, street, etc. The primary actuator includes wheels (or legs) of the robot. Sensors include a camera, sonar system, etc. A simple Model-based reflex algorithm can work in your case: function MODEL-BASED-REFLEX-AGENT(percept) returns an ...


2

Don’t think about it as the being proportional to something. Think about it this way: I’m now at . Where do I want to be at Time step so that the error decreases? For that, I need to know how the error changes when I make small steps to the left or right of If increases as I increase (that is, if , then obviously, I would want to move a little bit to ...


2

It is calculated the same way $b_1$ is calculated. Nearly following your notation, say your multiple linear regression function is $H(X_i) = b_0 + b_1x_{1,i} + ...+ b_nx_{n, i}$ for data instance $X_i=x_{1,i},...,x_{n, i}$ and weights $b_0,...,b_n$. And say your error function is $E(X,Y) = \sum_i(H(X_i)-Y_i)^2$ where $X$ is the collection of all data ...


2

Welcome to AI.SE @GundamOfOasis! Your intuition is right: this is fundamentally a problem for combinatorial search. You're also right that problems are created by the fact that not every move is valid at state. To fix this, you need to add a function that can determine whether a given state is valid or not, in addition to the usual function that checks ...


2

Yes! If you read ahead to the chapters in reinforcement learning in the same book, you'll see that the wompus world appears again there. Techniques like Q-learning can be used to solve it, and since Q-learning involves learning the shape of a function, a neural network can be employed as a function approximator. The basic idea is to treat this problem as an ...


2

$f(x) = x^2 + b$ is a polynomial (more precisely, a parabola) so it is continuous, thus, a neural network (with at least one hidden layer) should be able to approximate that function (given the universal approximation theorem). After a very quick look at your code, I noticed you aren't using an activation function for your dense layers (i.e. your activation ...


1

One term that would describe it is, "Automated navigation," but no one can easily guess what others might call it. The requirement is a little fuzzy, which doesn't help name it properly. We can go over a few of the more likely variants. The system learns a particular navigation path, in which the case is called, "Test Case Recording," because functional ...


1

There are two factors that will change the ability of a deep neural network to fit a given dataset: either you need more data, or a deeper and wider network. Since the pattern is only 2-d, it can likely be approximated by some sort of simple periodic function. A DNN can approximate periodic functions pretty well, so the issue is probably that you don't have ...


1

Welcome to AI.SE @EdouardLopez! Because Boston Dynamics is a private, for profit, company, we cannot know for sure how they achieve their results. However, we can examine the available public information and make educated guesses. In the information posted with the video, Boston Dynamics tells us that they use ... an optimization algorithm transforms ...


1

You are basically describing the way Google Translate works. There has been a lot of research in text alignment in the area of multi-lingual corpus linguistics. An early paper (with sourcode) is Gale and Church's A Program for Aligning Sentences in Bilingual Corpora (PDF). In linguistics these are called parallel texts. On the wikipedia page you will find ...


1

Artificiality Artificial means designed by humans. It is easy to explain why. Some would propose that artificial means not man made. However, that definition has shortcomings. It brings up the question of whether always defaulting word gender to one of two genders perpetuates gender imbalance. The verb to make is not what we mean when we use the word ...


1

Comparing Unlike Objects The comparison between a person and an artificial network cannot be made on an equal basis. The former is a composition of many things that the later is not. Unlike an artificial network sitting in computer memory on a laptop or server, a human being is an organism, from head to toe, living in the biosphere and interacting with ...


1

If you don't have non-examples of your pattern and don't have some kind of heuristic guide, unfortunately the answer is that you can't. "All sentences" will always be 100% compatible with your examples, and you'll never be able to collect evidence that disconfirms (or even decreases the likelihood) of that hypothesis. Even if you rule out that hypothesis by ...


1

What you have is called a classification problem with categorical features. That is, the features can be represented numerically, but the numbers have no relative meaning. Algorithms that rely on smooth function approximation will probably not work well here. These would include classic approaches to regression, and also function approximation via a neural ...


1

If your main issue is dealing with new vocabulary, you could try using a parts-of-speech tagger as a pre-processing step. You would then effectively discover relationships between "noun" and "verb", which does not change with new words. Taggers usually can handle unknown words by using contextual information. So you'd tag the words with their word class ...


1

Which do you think is the best method? As with most machine learning, each approach has its strengths and weaknesses, and other than a little bit of intuition: Policy-based methods are strong in large or continuous action spaces, and/or where there is a simple relationship between state and optimal action. E.g. controlling a robotic arm with continuous ...


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